What GOVERN 5.2 asks an AI team to prove about feedback
GOVERN 5.2 asks whether your team can show that outside input from relevant AI actors reaches the design and implementation of a system on a recurring basis, and that it is tied to risk tolerance levels and approval decisions. When someone asks how outside input changes an AI system that is in design or in production, the honest answer has to be a standing mechanism, not a meeting that happened once. Under NIST AI 100-1, the outcome is about regular feed-through: adjudicated input goes in, a decision is recorded, and the system changes or does not change for stated reasons. NIST AI 100-1 is voluntary, and NIST does not certify or endorse any organization's use of the framework, so the proof you build is evidence for your own governance and for the customers, auditors or regulators who ask about it. The distinction from GOVERN 5.1 matters here. GOVERN 5.1 covers collecting and weighing feedback about potential individual and societal impacts. GOVERN 5.2 adds the step that makes that feedback consequential: someone with authority adjudicates it, the system is assigned to a risk tolerance level by combining impact and likelihood, and the result is approval, conditional approval or disapproval. The Playbook also allows early decommissioning of systems whose risks cannot be mitigated, so the mechanism has to be able to end a project, not only adjust it. The risk this subcategory addresses is that feedback arrives without decision rights. Reviewers raise concerns, product owners absorb some and drop others, and nobody records why. The difficulty is that adjudication is often undocumented: the people who weigh input may not be the people who can change the design, and the reasoning lives in chat threads. Before you commit to an approach, confirm who holds the authority to adjudicate, which risk tolerance levels your organization has defined and how they are documented, and how a feedback item moves from receipt to a recorded change or a recorded refusal.
Which AI systems and teams fall under GOVERN 5.2
GOVERN 5.2 only works if the organization decides first which systems, people, models and risk levels the feedback loop covers. These four decisions set the size of the work, and each one should be written down and approved before the first feedback cycle runs.
AI systems in the inventory
Start with the AI inventory that GOVERN 1 asks for. Every system listed there needs a named owner and a known route for feedback to reach its design team, while systems that never reached production can usually be left out. Check whether shadow AI tools, embedded vendor features and pilots are in the list, because feedback channels that skip them leave gaps an assessor will find. Confirm the inventory is current before you size the loop.
Relevant AI actors who feed input
AI 100-1 Appendix A describes the tasks of each AI actor category. For GOVERN 5.2, the Playbook tags AI impact assessment, governance and oversight, and operation and monitoring as the relevant groups. Decide which people in each group can submit adjudicated input, who adjudicates it, and how often each group is consulted. Record the list so that a missing reviewer can be shown as a scoping decision rather than an oversight.
Third-party models and data
Decide whether models, datasets, APIs and hosted components supplied by vendors sit inside the feedback loop. Feedback about a vendor model often cannot change the model itself, so the loop should record what the team can change, such as prompts, retrieval sources, thresholds or guardrails, and what must go back to the supplier. Ask vendors what change notices, evaluation results and incident reports they provide, because those inputs determine what the team can adjudicate.
Risk tolerance levels in scope
Feedback should change design only in proportion to risk, so the loop needs defined risk tolerance levels. Assign each system a level by combining impact and likelihood, and decide which levels require approval, conditional approval or disapproval before feedback changes the system. Systems whose risks cannot be mitigated should have a documented route to early decommissioning. Ask the approving executive to confirm these thresholds in writing.
How an assessor tests GOVERN 5.2
This table walks through the test steps an internal or external reviewer would use for GOVERN 5.2, the evidence each step asks for, and the findings that usually come up. Read it as a checklist of what your program must be able to show, not as a certification process, since NIST does not certify AI RMF use.
| Test step | What the assessor checks | Evidence requested |
|---|---|---|
| Scope the sample | Selects AI systems across risk tiers, including at least one system the organization decided to decommission or conditionally approve | System inventory, risk tier assignments, list of feedback-generating reviews in the period |
| Trace feedback to adjudication | Follows each sampled feedback item from receipt through a documented adjudication decision by a named owner | Feedback log entries, adjudication records, reviewer identity and dates |
| Trace to design change or decision | Confirms each adjudicated item led to a design change, a documented rejection with reasons, or a recorded approval decision | Change tickets, design revision history, approval or disapproval records |
| Check risk tolerance use | Verifies risk tolerances are documented, were informed by applicable laws and standards, and were applied when assigning systems to approval levels | Written risk tolerance statement, impact and likelihood scoring worksheets, approval conditions |
| Check early decommissioning review | Looks for evidence that the team considered decommissioning when risks could not be mitigated, even where the outcome was to continue | Decommissioning criteria, meeting minutes or decision memos, decommissioning tickets where applicable |
| Test ticket and log integrity | Checks timestamps, ownership and closure status to confirm records were not edited after the fact | Ticketing system audit trail, export history, retention settings |
| Common finding: feedback logged but not adjudicated | Feedback entries with no owner, no decision or no closure reason, often for months | Aging report of open feedback items, backlog review notes |
| Common finding: risk tolerance not applied | Tolerance documented but approval decisions do not reference it, or tiers cannot be reproduced from the scoring | Approval records lacking tier reference, inconsistent scoring across systems |
| Common finding: no evidence of relevant AI actors | Feedback comes only from the build team, with no record of input from affected users, operators or other actors | Stakeholder list per system, consultation records, actor category mapping |
How to build the feedback-to-design loop step by step
This sequence turns outside input about an AI system into recorded decisions that change the design, approval status and release plan, so the team can show how adjudicated feedback was incorporated over time. It follows the GOVERN 5.2 outcome and the actions the Playbook suggests for it.
Define risk tolerance levels from applicable laws
Start by setting the risk tolerance levels the organization will use to classify its AI systems. Draw these from the laws, contracts and standards that apply to the system, and record who approved them, typically the accountable executive and the risk or compliance owner. The output is a written tolerance scale with defined levels, the criteria for each, and the decision owner for changes. Confirm the scale with legal and compliance before it is used on a live system, because the Playbook suggests tolerances informed by laws and standards.
Open a defined comment window for relevant actors
Publish the design or change under review to the relevant AI actors identified in the impact assessment, such as affected users, operators, domain experts and oversight staff. Give each group a defined window and a single intake channel, such as a ticketing queue, so every comment is logged with its source and date. The Playbook suggests giving relevant actors meaningful opportunities to comment on design. Output: a register of invited actors, the review window dates and the intake record for each submission.
Adjudicate each input with a recorded decision
Review every submitted comment and record a decision for it: accepted, accepted with modification, deferred with a reason, or rejected with a reason. Assign the adjudicator, who should be someone with authority over the design, and link each decision to the affected requirement, risk or test. Output: an adjudication log in which every input has a named decision, a rationale and a reviewer. Rejected inputs remain visible so the reasoning can be audited later.
Score each system's risk from impact and likelihood
Combine the adjudicated findings with the impact and likelihood estimates from the MAP function to place the system in one of the tolerance levels. Document the scoring method, the inputs used and any change from the prior assessment. Output: a dated risk rating for the system, with the scoring worksheet attached. Ask your assessors or internal risk function to confirm that the scoring method matches the tolerance scale defined in the first step.
Record approval, conditional approval or disapproval
Match the risk rating to a decision authority defined in governance. The decision is approval, conditional approval with named conditions, or disapproval. Each conditional approval lists the conditions, the owner of each condition and the evidence that closes it. Output: a signed decision record stored with the system's inventory entry. Disapproval blocks release until the design changes and the system is reassessed.
Feed approved changes into the backlog and release
Convert accepted changes and conditions into tracked work items in the engineering backlog, linked to the decision record. Require that each item passes through the normal release process, including testing and sign-off, before deployment. Output: a traceable chain from each feedback item to its backlog ticket, test result and release record. This is where many programs fail, because comments are accepted but never reach the code, so review the trace before the release is approved.
Set criteria for early decommissioning
Define in advance the conditions under which a system must be retired early, such as a risk that cannot be mitigated within the tolerance level, a failed condition that is not closed, or a repeated disapproval. The Playbook suggests allowing early decommissioning of systems whose risks cannot be mitigated. Output: written decommissioning criteria, the authority who may trigger them and a procedure for retiring the system and its data. Review these criteria at each adjudication cycle.
How the loop runs between releases
The feedback loop in GOVERN 5.2 only works if it runs on a fixed rhythm rather than when someone remembers to raise an issue. Set a review cadence for each AI system in its risk tier, and write it into the system record next to the approval decision. Higher-risk systems usually need a shorter cycle and a named trigger list, such as a model change, a new data source, a change in user population or a serious incident. The cadence itself is an internal choice. Confirm it against the risk tolerance your organization has documented, and against any law or contract that sets review expectations for the system. Ask the team to record each review as a dated entry that shows which feedback was received, which was adjudicated and what changed as a result. Adjudication needs a named chair with authority to decide, not only to collect comments. In most organizations this is the governance body that already owns AI approvals, with the product owner presenting and the risk function, legal, privacy and security reviewing. The chair should be able to decide which feedback is valid, which is out of scope and which needs more evidence, and should write down the reason for each outcome. Disagreements between the build team and reviewers go to a defined escalation path, usually the accountable executive who signed the original approval. Agree in advance how long an escalation may stay open and what happens if it is unresolved, for example whether the system stays in its current state or is paused. Confirm these rules in your own charter before the first review, because an undocumented escalation tends to default to whoever is loudest.
Approval status must be re-checked whenever the risk picture changes after deployment, not only at the scheduled review. Define the events that reopen an approval, including a material change in impact or likelihood, a new failure pattern reported by monitoring, or a change in the legal requirements that apply. When one of these events occurs, the system should move back to conditional approval, restricted use or disapproval, and the team should record which condition was met. The Playbook also points to early decommissioning for systems whose risks cannot be mitigated, so the adjudication body should be able to recommend retirement, and the owner should know how that decision is executed. Ask whether your tooling can show, for any system, the current approval status, the date it was last confirmed and the open feedback items. A ticketing or observability platform can hold those records, but the approval decision itself stays with your accountable people, and an independent assessor will expect to see that trail.
Evidence that satisfies GOVERN 5.2 and how to keep it current
GOVERN 5.2 is only as credible as the records behind it. The artifacts below show who gave feedback, how it was adjudicated, what risk tolerance applied and what decision followed, and each one needs a refresh cycle so it still reflects the system as it runs today.
Feedback log with adjudication records
The feedback log is the running register of input from relevant AI actors, with the source, date, affected system version and the adjudication decision for each item. An assessor will look for a traceable path from a comment to an outcome, including comments that were rejected and the reason. Review the log at a fixed interval, confirm every open item has an owner, and check that adjudicators are the people with authority to decide. A ticketing tool such as GLPI or Zammad can hold this record if each entry keeps its linked decision.
Risk tolerance documents
Risk tolerance documents set the reasonable limits that feedback is judged against, and the Playbook asks organizations to define them using laws and standards. Record the tolerance level for each system, the basis for it and who approved it. Re-check tolerances when the system's use changes, when a new law applies, or when monitoring shows the limit is being pushed. Keep superseded versions so you can show which tolerance governed a decision made at the time.
Impact and likelihood scoring
Scoring records show how each system was placed into a risk tolerance level by combining impact and likelihood. Keep the scoring method, the inputs, the scorer and the date, so the placement can be reproduced. Re-score when feedback reveals a new impact, when incident data changes likelihood, or when the system is retrained or extended to new users. A score that has not been revisited since launch is a common gap an assessor will question.
Approval, conditional approval and disapproval records
Each system needs a dated decision recorded as approval, conditional approval or disapproval, with the named approver and the conditions attached. Conditional approvals should list the specific conditions, the owner of each one and the date it must be met. Track conditions to closure, because an open condition that has lapsed is a finding. Disapproval records matter as much as approvals, since they show the process can stop a system.
Design change records linked to feedback
Design change records show that adjudicated feedback reached the system. Each change should cite the feedback log entry it answers, the affected component, the test that confirmed the change and the release in which it shipped. Check periodically that changes marked as done appear in the released version and that rejected feedback has not been quietly built in. Version control history and the change board record can serve as the link if they carry the feedback reference.
Decommissioning decisions
The Playbook suggests allowing early decommissioning of systems whose risks cannot be mitigated, so the record should show when that path was considered and what was decided. Keep decommissioning criteria written in advance, the decision, the date the system was withdrawn and what happened to its data and logs. Review the criteria when the risk tolerance changes. Confirm with your own counsel and records team how long these records must be retained.
What meeting GOVERN 5.2 costs in money and effort
This table breaks down the cost components of running GOVERN 5.2 feedback loops, showing who is paid for each, whether it is one-time or recurring, and what drives it up or down. Use it to build the questions you put to any quote, including where self-hosted open-source tools remove license spend. NIST publishes no price or effort figures for this subcategory, so every cost depends on your own scope.
| Cost component | Who is paid or who spends effort | One-time or recurring | What drives it |
|---|---|---|---|
| Process design for adjudication | Internal governance lead, possibly an implementation partner | One-time, revisited at each framework review | Number of AI systems in scope, number of relevant AI actor groups, how many risk tolerance levels you define |
| Tooling and licenses | Software vendor, or none if self-hosted open source is used | Licenses recur, open-source setup is one-time | Ticketing seat count, whether you run a helpdesk and an ITIL module separately, hosting in your own cloud or data center |
| Engineering time to link feedback to backlog items | Internal engineering or platform team, or a partner under a fixed-scope agreement | Recurring, scales with feedback volume | Change frequency, number of systems in production, how much feedback reaches design versus gets closed as out of scope |
| Governance meeting time for approval decisions | Risk, legal, product and system owners | Recurring | Number of approval, conditional approval and disapproval decisions per cycle, and how many risk tolerance levels require sign-off |
| Evidence upkeep | Compliance lead and system owners | Recurring | Volume of records kept for each adjudication decision, audit trail depth, and how often approvals are revisited |
| External review of the mechanism | Independent reviewer or auditor, engaged only if you commission one | Usually one-time per cycle, recurring if required by a contract or regulator | Whether a customer, regulator or certification body asks for the evidence, and the sampling depth they choose |
| Auditor sampling of feedback records | External auditor or assessor, where an assessment applies | Recurring with each assessment cycle | Number of AI systems sampled, number of feedback items per system, and how cleanly records link to decisions |
Who owns GOVERN 5.2 and what a partner can prepare
Your organization owns GOVERN 5.2 in full. The team that built or deployed the AI system sets the risk tolerance levels, decides how feedback from relevant AI actors is adjudicated, and makes the approval, conditional approval or disapproval decision for each system. Those choices reflect your legal obligations, your risk appetite and your accountability structure, so they cannot be delegated to a vendor or consultant. The framework's own suggested actions assume the organization defines tolerances informed by laws and standards and gives affected actors a meaningful chance to comment on design.
- Risk tolerance levels: Define the levels, the criteria for assigning a system to each one, and who signs off on them.
- Adjudication decisions: Record which feedback was accepted, rejected or deferred, and the reason for each outcome.
- Approval authority: Name the people who can approve, conditionally approve or stop a system, and the conditions attached.
- Early decommissioning: Decide when a system whose risks cannot be mitigated must be withdrawn.
Confirm that these decisions are written into a policy your executives have approved, because an assessor will ask who made them and when.
An implementation partner such as DeployOpen can prepare the systems that carry the GOVERN 5.2 process, under a fixed scope agreed in writing before work starts. The partner does not decide tolerances, adjudicate feedback or approve systems. Its role is to make the workflow repeatable and the records complete, so your decision makers can work from consistent evidence.
- Workflow tooling: Configure self-hosted ticketing or helpdesk tools, such as GLPI or Zammad, to route feedback, track status and capture adjudication outcomes.
- Evidence templates: Prepare forms for risk tolerance assignments, feedback records and approval decisions that map to your policy.
- Telemetry integration: Connect model and application traces, for example from Langfuse, to the records that show what changed in a system after feedback.
- Readiness review: Test the workflow against your own documented process and report gaps in writing.
Ask any partner to name the artifacts it will deliver, who in your organization will own each one after handover, and what ongoing support, if any, is included.
An independent auditor, assessor or certification body tests whether your GOVERN 5.2 process operates as described and issues any report or opinion. NIST does not certify or endorse organizations using the AI RMF, and no AI RMF certification exists, so no assessor can grant an AI RMF certificate. Any assurance report you receive must come from an independent party you have engaged, not from your implementation partner or from NIST.
- Design review: Check that tolerances, adjudication rules and approval authorities are documented and approved.
- Operating evidence: Sample feedback items and trace each one to a recorded adjudication and, where relevant, a design change.
- Independence: Confirm the assessor did not design the workflow it is testing, which protects the credibility of its conclusions.
- Reporting: Obtain any formal report or attestation directly from the assessor, using the scope and criteria that were agreed.
Confirm with the assessor which criteria it will test against and whether a recognized report type applies to your situation, because a readiness review and a formal assessment are different engagements.
Where GOVERN 5.2 programs usually fall short
GOVERN 5.2 asks for mechanisms that feed adjudicated input from relevant AI actors back into design and implementation. Most programs stall in the steps between collecting feedback and acting on it, where risk tolerance, approval decisions and re-review are either missing or undocumented. The gaps below are the ones to check first, each with a closing action.
Feedback collected but never adjudicated
Many teams gather comments through surveys, user reports or review meetings, but nobody is assigned to decide which input is valid, which affects the system, and which can be set aside. GOVERN 5.2 expects adjudicated feedback, so raw input that never reaches a decision does not count toward the outcome. Check whether each feedback item has a named owner, a recorded decision and a stated reason for that decision.
No link between feedback and design tickets
Adjudicated feedback often lives in a meeting note or inbox while engineering work sits in a separate backlog. Without a traceable link, the team cannot show that a concern led to a design change or was deliberately accepted. Check that every accepted item maps to a ticket, and that the ticket records the change made. GLPI's Assistance module, which supports tickets, problems and changes following ITIL practices, is one open-source option to confirm against your process.
Risk tolerance not defined
Adjudication has no reference point if the organization has not set reasonable risk tolerances informed by applicable laws and standards. Without them, one reviewer may accept a risk that another would reject. Check that tolerance levels are written down, that each AI system is assigned to one, and that the assignment combines impact and likelihood as the Playbook suggests.
Approval decisions undocumented
Approval, conditional approval and disapproval decisions are often made verbally or buried in chat threads. An assessor testing GOVERN 5.2 will ask who decided, on what evidence, and under which risk tolerance. Check that each decision record names the approver, the system version, the risk level, the conditions attached and the date. Recording these in a helpdesk such as Zammad, which brings communication into one ticket system, keeps the trail in one place.
No path to conditional approval or decommissioning
Programs often offer only approve or reject, so systems with unresolved risks either ship unchanged or drift on without a defined exit. The Playbook suggests allowing early decommissioning of systems whose risks cannot be mitigated. Check that conditional approval states the conditions, owners and review dates, and that a written decommissioning procedure exists and has been tested on at least one system.
No re-review after deployment changes
A decision made on one model version or dataset may not hold after a retraining, a new prompt, a changed tool connection or a shift in user population. Feedback loops that stop at launch leave these changes unchecked. Check that material changes trigger a defined re-review with relevant AI actors, and that production traces or logs give reviewers the evidence they need, for example through Langfuse traces that record inputs, outputs and tool executions.
Questions buyers ask about GOVERN 5.2 cost, effort and help
How much effort does GOVERN 5.2 take to put in place?
The effort depends on how many AI systems are in scope, how many relevant AI actors each one has, and whether a feedback process already exists. GOVERN 5.2 asks for mechanisms that let the team feed adjudicated input into design on a regular basis, so the work is mostly process design, a risk tolerance scheme, and recurring review meetings with recorded decisions. Costs are mainly staff time from the product, risk, legal and operations teams, plus any tooling. NIST does not publish effort figures for this subcategory, so ask any provider for a written breakdown by system and by activity before you commit.
Can we implement GOVERN 5.2 in-house without outside help?
Yes, an organization can do this in-house, because NIST's AI Risk Management Framework is voluntary and no outside party is required. The internal work is to set reasonable risk tolerances informed by applicable laws and standards, decide how relevant AI actors can comment on design, assign each system a tolerance level by combining impact and likelihood, and record approval, conditional approval or disapproval. Outside help is most useful for systems and infrastructure work, such as deploying a helpdesk or observability platform, and for reviewing evidence before an independent assessment. Teams without governance staff often choose a hybrid model.
What does an implementation partner need from our organization?
A partner needs access to the people and records that define how AI systems are built, approved and monitored. In practice that means a current inventory of AI systems, the owners who can approve risk decisions, existing risk tolerance statements, the channels through which users and affected staff already raise concerns, and any documented approval or change records. The partner also needs access to the systems that will hold the evidence, such as the ticketing and monitoring platforms, inside your own cloud account or data center. Ask the partner to confirm these inputs in the written scope so the schedule depends on items you control.
How long does readiness work for GOVERN 5.2 take?
Readiness timing depends on the number of systems in scope, the state of existing governance records, and how quickly decision-makers can approve risk tolerances. NIST does not supply a duration for this work, and no independent figure has been established for it in this guide. The most reliable way to estimate is to ask for a fixed-scope plan that lists each system, each deliverable and each dependency, with dates agreed in writing before work starts. Expect the schedule to depend heavily on how long it takes your leadership to decide risk tolerance levels.
Does an assessor certify GOVERN 5.2 or the AI RMF?
No. There is no AI RMF certification, and NIST does not validate or endorse any individual organization's use of the framework. No accreditation or conformity assessment program exists for it, so no certification body can issue a GOVERN 5.2 certificate. Any outside reviewer can only give an opinion about your evidence under the terms of their engagement. If a customer or regulator asks for proof, provide documented risk tolerances, approval records and feedback logs, and clarify what the reviewer actually examined.
Is the NIST AI RMF Playbook a checklist for GOVERN 5.2?
No. NIST states that the Playbook is neither a checklist nor a set of steps. It offers suggested actions, documentation prompts and references for each subcategory, including GOVERN 5.2, and organizations choose which suggestions fit their context. For GOVERN 5.2, the Playbook suggests defining risk tolerances, giving relevant AI actors meaningful chances to comment on design, assigning systems to tolerance levels, and allowing early decommissioning of systems whose risks cannot be mitigated. The Playbook's last content update is recorded as August 2023, and NIST's Playbook data is available for teams that want to track the suggestions in their own tools.
Which open-source tools support the feedback loop
This sequence shows how intake and tracking tools capture adjudicated feedback, and how measurement tools supply the evidence that feedback is weighed against. The output is a traceable record linking each decision to the system behavior it addressed.
Choose intake channels for external feedback
The organization decides which channels relevant AI actors use to send feedback, such as email, chat, telephone or social media. Zammad is one open-source option that brings those channels into a single ticket system under the AGPL-3.0-only license. Confirm with counsel and your own engineering team that AGPL-3.0-only obligations fit how you plan to host and modify the software. The step is complete when each channel is documented, owned, and routed to one queue.
Log feedback as tracked tickets
Each piece of feedback becomes a ticket that records the sender's role, the affected system, and the impact described. Staff who triage incoming messages capture the details in the ticket rather than in personal inboxes. The ticket is the record that later shows the feedback existed and was considered. Check that tickets can be linked to the specific system version and risk category they concern.
Adjudicate feedback through a governed process
A named group reviews each ticket and decides whether it is valid, requires design change, or is out of scope. GLPI's Assistance module, distributed under GPL-3.0-or-later, follows ITIL practices for tickets, problems and changes, and it offers plannings, statistics, recurrent tickets and a Kanban view. These features can support triage, assignment and change tracking. Confirm the module's workflow and approval capabilities in your own deployment before relying on them for approval records.
Connect feedback to risk tolerance decisions
Adjudicated items are tied to the system's assigned risk tolerance level and to the approval, conditional approval or disapproval decision the organization records. This step uses the risk tolerance and approval outcomes defined under GOVERN 1 and MAP 1. The output is a decision record in the ticket or linked change request that names the approver and the conditions attached.
Measure system behavior before and after change
Engineers collect evidence that shows how the system behaves before and after a design change. Langfuse, distributed under the MIT license, captures traces of each request, including LLM calls, retrieval steps, tool executions, timing, inputs, outputs, token usage and metadata. These traces let the team check whether a change affected the behavior that feedback raised. Confirm that trace capture covers the data your privacy obligations allow you to store.
Probe for vulnerabilities with adversarial tests
For language model components, the team runs adversarial probes to test for issues such as hallucination, data leakage, prompt injection and jailbreaks. garak, distributed under Apache-2.0, writes a JSONL report of every attempt and evaluation, plus a hit log of attempts that found a vulnerability. Test results that confirm or refute a concern raised in feedback are attached to the ticket. Probe coverage is limited to the categories garak tests, so teams should document what is not covered.
Close the loop with a recorded outcome
Each ticket closes only after the design or implementation change is made, the measurement evidence is attached, and the feedback sender receives a response where the process allows it. The closed record links the adjudicated input, the decision, the change, and the test or trace evidence. Review the closed set periodically to confirm feedback is being incorporated, not just logged. The output is an auditable trail that assessors can sample.
How DeployOpen scopes GOVERN 5.2 readiness
DeployOpen scopes GOVERN 5.2 readiness through a fixed-scope written agreement that names the AI systems in scope, the relevant AI actors whose adjudicated feedback must reach design, and the decision points where that feedback is applied. Work happens inside the client's own cloud accounts, data centers or air-gapped networks. DeployOpen can configure the ticketing and feedback paths the team will use, such as GLPI's Assistance module for tickets, problems and changes, or Zammad for bringing external feedback from email and other channels into one queue, and can set up tracing with Langfuse or red-team probing with garak where the system is a language model application. The organization still decides its risk tolerance levels, its approval, conditional approval and disapproval rules, and who holds the authority to decommission a system. DeployOpen does not make those decisions or issue any audit report, attestation or certification. Independent auditors and certification bodies do that work.
Ask the partner for a written deliverables list that maps each agreed artifact to a GOVERN 5.2 action from the Playbook: a documented risk tolerance method, a record of how each AI actor was invited to comment on design, the scoring that places each system in a risk tolerance level, and a log of approvals, conditions and early decommissioning decisions. Confirm which records the ticketing platform keeps by default and which must be added by configuration. Also confirm who in your organization signs off on the adjudication outcomes, because an assessor will ask who made each decision, not only what was recorded. The Playbook itself was last updated in August 2023 and NIST says it will be revised after the AI RMF is revised, so confirm current wording with NIST before fixing the scope.
Plan the cost in two parts. The one-time portion covers scoping, configuring the feedback and approval workflow, and producing the first evidence set. The recurring portion covers reviewing feedback cycles, updating risk tolerance records when laws or standards change, and retesting systems after material changes. Retainers cover the recurring work, and their scope should state how often feedback is reviewed, who attends the review and how changes to a system's risk tolerance level are handled. Request a quote that separates these items, names the systems and AI actor categories covered, and states what is excluded, such as independent assessment fees. Any fee or duration figure in the quote comes from the written scope, not from a DeployOpen price list, which the company does not publish.
