An AI procurement checklist is the shortest route from “the sales team wants this tool” to a decision your organisation can defend a year later. Buying artificial intelligence is not like buying a database. The product keeps changing after the contract is signed, the data you feed it may never come back, and the three teams who have to approve it are each looking at a different kind of danger. Without a shared list of questions, legal, security and IT end up reviewing the same purchase three times, in three documents, reaching three conclusions that nobody reconciles.
This guide sets out a complete AI procurement checklist for legal, security and IT teams, organised the way a real purchase actually moves: intake and triage, then three parallel reviews, then evidence, then scoring, then a contract and a recorded decision. It is written for the person who has been handed a vendor demo and a deadline, not for a standards committee.
The structure matters more than the length. A ten-question review that everybody completes beats a hundred-question questionnaire that stalls in someone’s inbox for six weeks, so this AI procurement checklist tiers the depth of scrutiny to the risk of the use case. Low-risk tools clear in days. High-risk tools get the full treatment, and the business understands why.
Table of contents
- Why an AI procurement checklist is different from software buying
- Who owns what on the AI procurement checklist
- Stage one: intake and triage before anyone meets a vendor
- The legal section of the AI procurement checklist
- The security section of the AI procurement checklist
- The IT and architecture section of the AI procurement checklist
- Evidence to demand instead of assurances
- Contract clauses worth insisting on
- Scoring the AI procurement checklist and reaching a decision
- Running the AI procurement checklist without slowing the business down
- Common mistakes that weaken an AI procurement checklist
- Frequently asked questions about the AI procurement checklist
- References
Why an AI procurement checklist is different from software buying
Traditional software due diligence assumes a fixed product with predictable behaviour. Every assumption in that sentence fails for AI systems, which is why an AI procurement checklist needs its own questions rather than three extra rows bolted onto the existing vendor form.
The product changes after you buy it
A conventional application behaves the same on the day you renew as it did on the day you signed. A hosted model can be retrained, re-tuned, re-routed to a different underlying provider or silently upgraded between your pilot and your rollout. The capability you tested is not necessarily the capability you are running, so an AI procurement checklist has to ask how changes are communicated, versioned and rolled back rather than assuming stability.
The vendor is rarely the whole supply chain
Most AI products are a thin layer over someone else’s foundation model, running on someone else’s cloud, enriched by someone else’s data broker. Your contract is with the layer. Your risk sits underneath it. A serious AI procurement checklist asks the vendor to name the model providers, the hosting regions and the sub-processors, then asks what happens when one of them changes.
The failure mode is probabilistic, not binary
Ordinary software is either working or broken. An AI system can be confidently, plausibly and consistently wrong while every dashboard stays green. That changes what “acceptance testing” means: you are not checking that a function returns the right value, you are measuring an error rate against a threshold you agreed in advance. An AI procurement checklist that never mentions accuracy targets has skipped the most important number in the deal.
Three teams are asking three different questions
Legal wants to know who owns what and who pays when it goes wrong. Security wants to know what an attacker can reach through this system. IT wants to know what it costs to run, integrate and eventually replace. Those are not competing concerns, they are three views of the same purchase, and the point of a single AI procurement checklist is to make them one conversation instead of three sequential ones.
| Dimension | Conventional software purchase | AI system purchase |
|---|---|---|
| Behaviour over time | Stable between releases | Shifts with model updates |
| Acceptance test | Pass or fail | Error rate against a threshold |
| Supply chain visibility | Usually one vendor | Model, host and data layers |
| Data risk | Storage and access | Storage, access and training reuse |
| Cost shape | Per seat, predictable | Per token or per action, variable |
| Exit difficulty | Export the data | Data, prompts, tuning and workflow |
| Regulatory surface | Data protection | Data protection plus AI-specific rules |
Who owns what on the AI procurement checklist
The fastest way to stall a purchase is to leave ownership vague. Each section of the AI procurement checklist needs a named owner who can say yes, say no, or say yes with conditions, and one person who owns the final record.
Legal owns the words that survive the relationship
Legal is accountable for the contract, the data processing terms, the intellectual property position and the regulatory classification of the use case. Their output is not an opinion, it is a set of clauses that must appear, a set that must not, and a note of which fallbacks are acceptable if the vendor pushes back.
Security owns the blast radius
Security is accountable for what an attacker or a mistake can reach through the system. That includes the obvious controls and the AI-specific ones: what the model can read, what tools it can call, what it can do without a human confirming, and how quickly you would find out if something went wrong. Their output is a risk rating and a list of mandatory mitigations.
IT owns the operating reality
IT is accountable for identity integration, data flows, capacity, cost and the eventual migration away. They are also the team that inherits the tool when the enthusiasm fades, which is why an AI procurement checklist should give them an explicit veto over anything that cannot be supported, monitored or switched off cleanly.
The business owns the justification
Someone has to state what problem is being solved, what it is worth, and what happens if the tool is not bought. Without that, every review question becomes a debate about whether the purchase is needed at all. Requiring a one-page justification before the AI procurement checklist starts filters out a surprising share of requests.
One decision record, three sign-offs
Three parallel reviews converge into a single record: the use case, the risk tier, each team’s verdict, the conditions attached, the person accountable and the review date. That record is the deliverable. If it lives only in an email thread, the AI procurement checklist has produced compliance theatre rather than governance, and your IT governance forum will have nothing to review.
| Checklist section | Owner | Decides | Typical evidence |
|---|---|---|---|
| Use case and value | Business sponsor | Is it worth doing | One-page justification |
| Data classification | Data owner | What may be shared | Classification record |
| Contract and IP | Legal | Terms accepted or not | Redlined agreement |
| Privacy and lawful basis | Legal with the DPO | Whether a DPIA is needed | Screening and DPIA |
| Security controls | Security | Risk rating and mitigations | Report, certificates, tests |
| Architecture and support | IT | Can we run it | Integration and support plan |
| Cost and commercials | IT with finance | Is the model affordable | Modelled annual cost |
| Final decision record | Procurement lead | Approve, condition, reject | Signed decision note |
Stage one: intake and triage before anyone meets a vendor
Half the value of an AI procurement checklist is created before a single vendor question is asked. Triage decides how much scrutiny the purchase deserves, and getting that right is what keeps the process fast enough to be used.
Write down the job to be done
Ask the requester to describe the task in plain language: what goes in, what comes out, who acts on the output and what happens if the output is wrong. Two sentences is enough. This single step exposes the requests where the real problem is a broken process, and it gives every later question something concrete to attach to.
Classify the data before you classify the vendor
The data decides almost everything else. Personal data, special category data, client confidential material, source code and regulated records each pull in different obligations. Classify first, because a tool that only ever sees public marketing copy does not need the same AI procurement checklist depth as one that reads customer case files.
Decide the risk tier in ten minutes
Combine data sensitivity, autonomy and reach. A tool that drafts text for a human to approve is a different proposition from one that sends messages to customers or changes records without review. Three tiers are enough for most organisations, and the tier drives which sections of the AI procurement checklist are mandatory.
Check whether a rule already answers the question
Some requests can be closed immediately: the data cannot leave the jurisdiction, the use case is prohibited by policy, or an approved tool already does the job. A published AI acceptable use policy turns those into a two-minute answer rather than a six-week review, and it is the cheapest accelerator you can give the AI procurement checklist.
Search the inventory for what you already own
A striking share of AI requests duplicate a capability the organisation already licenses inside an existing suite. Checking an AI system inventory before starting due diligence avoids paying twice and removes a new vendor relationship from the estate.
The legal section of the AI procurement checklist
Legal review is where an AI purchase either becomes a defensible commercial relationship or a set of assumptions written down by the vendor’s marketing team. These questions belong in the legal half of every AI procurement checklist.
Who owns the inputs, the outputs and everything derived from them
Establish in writing that you retain ownership of the data you submit, that you own or are granted an unrestricted licence to the outputs, and that the vendor gains no rights over derived material. Ambiguity here is common and expensive, because output ownership determines whether you can use the results commercially at all.
Training rights and the model improvement clause
The most consequential sentence in many AI contracts is the one granting the vendor a right to use customer data to improve its services. Require that training on your data is off by default, that any exception is opt-in and specific, and that the commitment applies to sub-processors. A vendor unwilling to write that down has answered a different question than the one you asked.
Data processing terms, transfers and sub-processors
You need a data processing agreement that names the processing purposes, the retention periods, the international transfer mechanism and the full sub-processor list, with notice before that list changes. Transfers outside the UK or the European Economic Area need a lawful mechanism and a transfer risk assessment, and the AI procurement checklist should require both as documents rather than assurances.
Confidentiality that survives the prompt
Standard confidentiality clauses were written for documents shared with people, not text pasted into a model. Make explicit that prompt content, uploaded files and retrieved context are confidential information, that they are not retained beyond a stated window, and that they are excluded from any human review programme unless you have agreed to it.
Indemnities that cover AI-specific harm
Ask what the vendor indemnifies. Intellectual property infringement arising from generated output is the clause worth arguing over, and several major providers now offer it. Check the conditions attached, because indemnities that only apply when you use unmodified default settings can evaporate the moment you tune anything.
Liability caps measured against the real exposure
A liability cap equal to twelve months of fees is normal and is often irrelevant to the harm an AI system can cause. Compare the cap to the plausible cost of a data incident or a bad automated decision at scale, and where the gap is indefensible, either negotiate a super-cap for data and IP claims or reduce the scope of what the tool is allowed to touch.
Regulatory classification and the AI Act question
Decide early which regulatory regimes apply. Under the EU AI Act, obligations follow the risk category of the use case, and deployers of high-risk systems carry duties of their own. Sector rules may add more. The AI procurement checklist should record the classification, the reasoning and the person who made the call, because that reasoning is what a regulator will ask to see.
Termination, exit and what happens to your data
Agree in advance how the relationship ends: notice periods, the export format for your data, the deletion timetable, the certificate of destruction and what happens to fine-tuned artefacts derived from your material. Exit terms are cheap to negotiate before signature and nearly impossible afterwards, which is why an AI vendor lock-in exit plan belongs in the same conversation as the purchase order.
The security section of the AI procurement checklist
Security review answers one question in many forms: what can this system reach, and what would happen if it were misused. The security half of the AI procurement checklist should be as specific as any other third-party assessment, plus the controls that only matter for AI.
Tenancy, isolation and administrative control
Establish whether your data sits in a dedicated tenant or a shared one, how isolation is enforced, and which vendor staff can access your environment. Ask how privileged access is approved and logged. These are ordinary questions, and they still catch a meaningful number of AI vendors whose platform matured faster than their controls.
Identity, authentication and role design
Single sign-on with your identity provider, enforced multi-factor authentication, and role definitions that map to real job functions are minimum expectations. Confirm that administrative roles can be assigned to named individuals and that de-provisioning happens through your directory rather than a vendor support ticket.
Data residency, retention and deletion evidence
Ask where data is stored, where it is processed, how long prompts and outputs are retained, and how deletion is verified. Vendors frequently retain prompt logs for abuse monitoring far longer than customers expect, so require the retention period as a number and put it in the contract rather than the AI procurement checklist alone.
Prompt injection, tool access and agent permissions
Any system that reads untrusted content and can also take actions is exposed to prompt injection. Ask what the model is permitted to do without human confirmation, how tool access is scoped, and what testing the vendor performs against injection and jailbreak techniques. The published guidance on large language model risks gives you the vocabulary to make that a technical conversation rather than a reassuring one.
Output handling and human oversight
Decide which outputs require a human decision before they take effect, and confirm the product can enforce that. A tool that supports approval steps only in its enterprise tier is a different purchase from one that enforces them everywhere. This is also where an AI procurement checklist should capture the accuracy threshold and who monitors it after go-live.
Model provenance and supply chain
Ask which models are used, who provides them, whether they can change without notice, and whether any component is self-hosted from an open weights repository. Supply chain questions that are routine for software libraries are still unusual for models, and they belong on every AI procurement checklist at tier two and above.
Certification, testing and independent assurance
Request the current certifications and read the scope rather than the logo. Confirm whether the AI product is inside the scope of the ISO 27001 statement of applicability or the SOC 2 report, because a certificate covering the corporate platform tells you little about the new feature. Recent penetration test summaries and a remediation record are worth more than any certificate.
Logging, monitoring and incident notification
You need to know what is logged, whether you can export it into your own tooling, and how fast the vendor commits to notifying you of an incident. Twenty-four to seventy-two hours is the negotiating range for most enterprise agreements. Tie those commitments to your own AI incident response plan so the handover is rehearsed rather than improvised.
Reuse the questionnaire you already have
Most of this overlaps with existing supplier assurance work. Rather than inventing a parallel process, extend your third-party cybersecurity questionnaire with an AI annexe. Reviewers stay in familiar territory and the AI procurement checklist inherits an evidence trail that auditors already recognise.
The IT and architecture section of the AI procurement checklist
IT inherits the tool. The IT section of the AI procurement checklist is therefore about whether the thing can be run, integrated, paid for and eventually removed without a project.
Identity integration and licence sprawl
Confirm the product supports your identity provider properly, including group-based provisioning and automated de-provisioning. Tools that require manual user administration accumulate orphaned accounts within months, and orphaned accounts on a system holding company data are exactly the finding you do not want in an audit.
Integration surface and interface stability
List the systems the tool must read from and write to, and check whether integration happens through supported connectors, a documented API or a browser extension nobody controls. Ask about versioning policy and deprecation notice periods, because an AI vendor moving quickly can break an integration with a release note.
Performance, capacity and rate limits
Get the published rate limits, the concurrency ceiling and the behaviour under throttling in writing. A pilot with twelve users tells you nothing about four hundred. Ask what happens when the limit is reached: queueing, degradation or errors, and whether limits can be raised without renegotiating the entire agreement.
The cost model and the runaway spend question
Consumption pricing turns capacity planning into a financial control problem. Model the expected, plausible and capped-worst monthly cost, and ask what mechanisms exist to enforce a ceiling. If the vendor offers no hard limit, you need one on your side, which is the same discipline described in our guide to AI cost governance and a recurring theme in every mature AI procurement checklist.
Support, escalation and service levels
Check the support hours against your working day, the escalation path, the definition of a severity one incident and whether the service level covers availability only or also model quality. Very few AI vendors commit to accuracy in a service level, and knowing that before signature is more useful than discovering it during an outage.
Observability and the ability to answer questions later
You will be asked who used the tool, on what data, and with what result. Confirm you can answer that from exportable logs. Systems that cannot produce a usage trail push the burden onto manual record keeping, which fails quietly and is usually discovered during an investigation.
Exit paths and portability
Ask precisely what you can take with you: data, prompts, configurations, fine-tuning artefacts and evaluation results, and in which formats. Then ask how long a migration would take. If the honest answer is that the workflow could not be reproduced elsewhere, that is not a reason to walk away, but it should raise the price you are willing to pay for a longer notice period.
Evidence to demand instead of assurances
The difference between an AI procurement checklist that works and one that generates paperwork is whether answers are evidenced. Questionnaires reward confident writing. Evidence rewards actual control.
Ask for documents, not adjectives
“Enterprise grade security” is not an answer. The current SOC 2 Type II report, the ISO 27001 certificate with its statement of applicability, the data processing agreement, the sub-processor list, the retention schedule and a penetration test summary are answers. Ask for them by name and set a deadline.
Read the scope, not the badge
Certificates are scoped to systems, sites and time periods. Check that the certificate covers the product you are buying, that the report period is current, and that exceptions in the report have been remediated. Reading the scope statement is the single highest-value ten minutes in the whole AI procurement checklist.
Test the claims that matter to you
Where accuracy is the point of the purchase, run your own evaluation on your own representative data before signature, and record the result as the baseline. Vendor benchmarks are marketing. A modest evaluation set built from real cases is the only measurement that will mean anything at renewal.
Handle “we cannot share that” proportionately
Some documents genuinely cannot be shared without an agreement in place, and a summary letter or a supervised review is a reasonable compromise. A blanket refusal to evidence anything is itself a finding, and the AI procurement checklist should record it as one rather than quietly moving on.
| Evidence requested | Tier 1 | Tier 2 | Tier 3 |
|---|---|---|---|
| Security certification and scope | Certificate only | Certificate and scope | Full report reviewed |
| Data processing agreement | Standard terms | Reviewed by legal | Negotiated |
| Sub-processor list and notice | Published list | List plus notice terms | Right to object |
| Penetration test summary | Not required | Latest summary | Summary and remediation |
| Privacy screening or DPIA | Screening note | Screening note | Completed DPIA |
| Accuracy evaluation | Not required | Vendor figures | Own test on own data |
| Exit and portability plan | Export documented | Export tested | Migration costed |
| Model and provider disclosure | Named in writing | Change notice agreed | Change approval right |
Contract clauses worth insisting on
By the time redlines are exchanged, the AI procurement checklist should already have told legal which clauses matter for this specific purchase. These are the ones that repay the negotiating effort most often.
The clauses that carry the most risk
Training restrictions, output ownership, retention limits, sub-processor notice, incident notification windows, audit rights and exit assistance form the core. Each has a realistic fallback position, and knowing the fallback before the call prevents a negotiation from stalling on a point you were always going to concede.
Write the AI-specific obligations into the schedule
Generic terms rarely cover model change management, evaluation access or human oversight requirements. Put them in a technical schedule with concrete numbers: notice periods in days, retention in days, accuracy thresholds as percentages. Schedules are easier to update at renewal than clauses buried in the master agreement.
Align the security clauses with the ones you already use
If you already maintain a set of supplier contract security requirements, extend them rather than drafting a parallel set. Consistency across suppliers is what makes contract obligations enforceable in practice, because your own teams only have to remember one standard.
| Clause | Position to open with | Acceptable fallback |
|---|---|---|
| Training on customer data | Prohibited entirely | Opt-in, specific and revocable |
| Output ownership | Customer owns outputs | Perpetual unrestricted licence |
| Prompt and log retention | Zero retention option | Stated maximum in days |
| Sub-processor changes | Prior approval | Notice with right to terminate |
| Incident notification | Within 24 hours | Within 72 hours |
| IP infringement indemnity | Uncapped for outputs | Capped, with clear conditions |
| Model change management | Approval before change | Notice and rollback window |
| Exit assistance | Defined and priced | Defined, chargeable at day rate |
Scoring the AI procurement checklist and reaching a decision
A checklist without a scoring rule produces a pile of answers and no decision. Scoring converts the AI procurement checklist into an outcome that a governance forum can review consistently.
Weight the sections by risk tier
At tier one, weight IT supportability heavily and keep legal review to the standard terms. At tier three, data protection and security dominate, and a failure on either is disqualifying regardless of the total. Publishing the weights in advance stops the scoring becoming an argument about the scoring.
Use three outcomes, not two
Approve, approve with conditions, and reject. Most real decisions are the middle one, and forcing a binary answer either blocks useful work or waves through unresolved risk. Conditions must be specific, owned and dated, otherwise they are wishes.
Make disqualifying findings explicit
Some answers end the conversation regardless of the score: no lawful basis for the processing, training on customer data with no opt-out, no incident notification commitment, or a refusal to name sub-processors. Listing those in the AI procurement checklist saves everyone the effort of scoring a purchase that cannot proceed.
Record the decision so it survives staff turnover
The decision note should fit on one page: what was bought, for what purpose, on what data, at what risk tier, with which conditions, approved by whom, reviewed on what date. In eighteen months that page is the only thing standing between your organisation and a reconstruction exercise, and it feeds directly into your AI risk assessment template.
Running the AI procurement checklist without slowing the business down
A review process that takes three months will be bypassed, and shadow purchases are worse than imperfect approved ones. Speed is a control, not a concession.
Tier the depth of review to the risk
The single biggest determinant of cycle time is how much of the AI procurement checklist applies. A tier one tool that touches no personal data should need a short form, an identity check and a cost note. Reserve the full process for the purchases that can actually hurt you.
Pre-approve a catalogue
Publish a list of tools already assessed and approved for defined data classes. Most requests are then answered instantly, and the review capacity you free up goes to the genuinely novel purchases. The catalogue also gives you a credible answer when someone asks why their request was refused.
Set a service level for your own reviews
Commit to a response time for each tier and publish performance against it. Nothing drives shadow AI faster than a review process with no visible end date, and nothing builds trust in an AI procurement checklist faster than meeting the deadline you set yourself.
Run the three reviews in parallel
Legal, security and IT should start at the same time from the same intake record. Sequential reviews add weeks and produce the same answer. A shared document with three sections and three owners is enough infrastructure for most organisations.
Re-review on a trigger, not only a calendar
Annual reviews miss the changes that matter. Re-open the AI procurement checklist when the vendor changes model provider, when the use case expands, when new data classes are added, or when an incident occurs. Calendar reviews then become a backstop rather than the only control.
Borrow the discipline from vendor due diligence
None of this is unique to AI, and teams that already run structured AI vendor due diligence will recognise most of the mechanics. The AI-specific questions are an extension of an established practice, not a replacement for it.
Common mistakes that weaken an AI procurement checklist
These five patterns show up repeatedly in organisations that have a process on paper and still end up surprised.
Reviewing the vendor instead of the use case
The same tool can be trivial in one department and high risk in another. Assessing the vendor once and approving it everywhere is how confidential records end up in a system that was cleared for drafting social posts. Approvals belong to use cases, and the AI procurement checklist should be completed per use case, with the vendor assessment reused.
Treating a certificate as an answer
A certificate proves a scoped audit happened. It does not prove the AI feature you are buying was in scope, or that the finding you care about was tested. Certificates narrow the questions you need to ask, and an AI procurement checklist that stops at the certificate has skipped the questions it narrowed you down to.
Letting the pilot become production
Pilots run on generous assumptions: a small user group, synthetic data, a temporary agreement. Production inherits none of those conditions and all of the momentum. Require an explicit gate between pilot and rollout, and re-run the parts of the AI procurement checklist that the pilot deliberately skipped.
Ignoring the tools people already bought
Individual subscriptions expensed on a card are already processing company data. A procurement process that only looks forward leaves that exposure untouched. Amnesty periods, a discovery exercise and a clear path to approval bring those tools into the process instead of driving them further underground.
Never re-opening a decision
Approvals age badly in a market that changes quarterly. A vendor that swapped model providers, expanded data collection or changed its retention policy is no longer the vendor you assessed. Without a trigger-based re-review, the AI procurement checklist becomes a historical document rather than a control.
Frequently asked questions about the AI procurement checklist
How long should an AI procurement checklist be?
Long enough to cover the risk tier and no longer. A practical structure is roughly ten questions at tier one, thirty at tier two and sixty or more at tier three including a full DPIA. Length is not the measure of quality; completion rates and the number of findings acted on are.
Do we need a DPIA for every AI tool?
No. A DPIA is required where processing is likely to result in a high risk to individuals, which many low-impact tools are not. Run a short screening test on every purchase, record the outcome either way, and escalate to a full assessment when the screening indicates it.
Who should own the AI procurement checklist overall?
Whoever owns supplier assurance today, usually procurement or IT, with legal and security as named contributors. Creating a separate AI governance function that duplicates supplier review tends to add delay without adding scrutiny, particularly in organisations under a few thousand people.
How do we assess AI features inside tools we already use?
Treat the new feature as a new purchase at the use case level. The vendor relationship, contract and security assessment may already exist, but the data flows and the autonomy are new, so the relevant sections of the AI procurement checklist still apply before the feature is enabled.
What if the vendor refuses to change their standard terms?
That is common with large providers and smaller purchases. Where terms cannot move, control the risk on your side: restrict the data classes allowed into the tool, reduce the autonomy granted, add monitoring, and record the residual risk as accepted by a named owner rather than pretending it was mitigated.
Should open source or self-hosted models skip the checklist?
No, though the questions shift. Contract and sub-processor questions shrink while model provenance, licence terms, patching, hosting security and your own accountability grow. The AI procurement checklist stays the same shape; the weight simply moves from the vendor to your own team.
How does this fit with ISO 42001 or the EU AI Act?
Both expect documented, repeatable decisions about AI systems, which is exactly what a completed AI procurement checklist produces. If you are working towards ISO 42001 certification, the decision records from procurement become part of the evidence base rather than a separate exercise.
References
Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence
NIST AI Risk Management Framework
NIST Generative AI Profile, NIST AI 600-1
ISO/IEC 42001 Artificial Intelligence Management Systems
ICO Guidance on Artificial Intelligence and Data Protection
ICO Data Protection Impact Assessments
NCSC Guidelines for Secure AI System Development
OWASP Top 10 for Large Language Model Applications
MITRE ATLAS Adversarial Threat Landscape for AI Systems
Cloud Security Alliance Cloud Controls Matrix and CAIQ
NIST SP 800-161r1 Cybersecurity Supply Chain Risk Management
UK Government Guidelines for AI Procurement
European Commission Standard Contractual Clauses
EDPB Opinion 28/2024 on data protection aspects of AI models
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