2026 AI Model Registry: Linking Versions, Evaluations, and Deployments to Every Decision

The subject “Register of AI models” must lead to proof, not just to deployment: the expected effect must be measurable and reversible.
Frame the “model identifier” point, check the “evaluation set” point, then decide with an explicit reference measurement.

1. Key figures

These benchmarks limit the decision to a register of usable models; they don't take it for you. A published value describes a precise perimeter, a date and sometimes a population different from yours. Read it as a constraint to be tested, not as the promise of an automatic effect. The decision can be reviewed.

If the measurement diverges, the sample remains representative: for this subject, the first source leads to the following operational reading: “An evaluation must cover deployment conditions, monitoring and documentation. » The second reference in the table must also be compared to your perimeter and a local measurement. This distinction between external reference and local measurement protects the analysis against easy extrapolations.

2. Read the sources without overinterpretation

Once the baseline has been established, the incident is subject to review: a source is useful when a reader simultaneously understands what it states, the scope it covers and the limit of extrapolation. The five benchmarks below are therefore reread as decision markers, never as causal promises.

For the scope “a register of usable models”, external data can only be used to decide if its scope, date, unit and limit are explained. The review should separate what the source establishes, what the team infers, and what a local test still needs to demonstrate.

Concretely, the proof sheet preserves the organism, the title, the URL, the date of consultation, the population, the unit, the method and the reservation of interpretation. It then indicates the decision that the benchmark informs and the local observation capable of contradicting this benchmark. In this file, attach this register to “model identifier” and entrust its review to “Professions”. Data without a documentary owner ages silently; data with a revision condition remains controllable and can be cited without losing its context.

2.1. Benchmark 1

NIST — AI Risk Management Framework provides the hint “4 functions” here. This information informs a choice; it does not, by itself, demonstrate that the same effect will appear in your context. The measurement precedes arbitrage.

2.2. Bench 2

The NIST AI Resource Center Reference — AI RMF publishes “4 Functions”. Before making a decision, check the date, the population covered and the possibility of replicating the measure locally. The roles are distinct.

2.3. Bench 3

The source European Commission — AI Act article 4 locates the terminal “article 4” in the field “personnel and service providers using AI systems in the European Union”. It provides an external reference to the diagnosis; it does not replace either a local reference measurement or the analysis of exceptions. These mistakes are costly.

2.4. Benchmark 4

The “2 injection pathways” milestone, published by OWASP GenAI — Prompt Injection, falls under the “LLM applications and connected agents” scope. It helps to formulate a testable hypothesis, without transforming an external value into an automatic objective. Control remains human.

2.5. Bench 5

European Commission — AI Act article 14 documents “5 human capabilities”. The exact range is shown in the previous table; keep it when comparing this data to your own operations, populations and periods. Nuance matters here.

3. Reusable citation sheet

Outside of the nominal scenario, the observed field remains stable: a robust quotation must be able to be repeated without losing its author, its date, its scope or its limit. The sheet below isolates these elements and links them to a specific decision; it prevents a correct figure from becoming misleading after extraction from its context.

4. Introduction: framework the primary risk

A deployment may appear successful while the "model identifier" processing remains incomplete, the "evaluation set" dependency remains fragile and the "deployment configuration" check is still missing. The discrepancy often only appears at “change log” time.

The concrete risk takes the following form: a list of API without traceability of actual changes. This problem cannot be corrected either by an activated option or by an additional dashboard; it requires a perimeter, a person responsible and contradictory proof.

When a dependency changes, the trace remains auditable: our position is therefore clear: the system only has value if the announced effect is observable. The comparison must relate to the situation before the change, then to the same segments after the test. Each step leaves a trace.

5. Actors and responsibilities

This distribution avoids confusing execution and responsibility. The first operational responsibility falls to the “Professionals” function; the “AI and data team” function provides separate control. The decision is only defensible if each actor knows what it measures, what it authorizes and what it takes back when the accepted limit is crossed. The discrepancy deserves an explanation.

6. Definition: exploitable model register

In this guide, the scope “an exploitable model registry” combines the points “model identifier”, “evaluation set”, “deployment configuration” and “change log”. The objective is to obtain each behavior in production linked to a version, a test and a maintainer; the decision is based on coverage of deployments by evidence and rollback capability.

At the next milestone, the convincing element remains linked to the decision: the definition is therefore operational: it names the components, the desired effect, the indicator and the limit. A reader can quote it without having to reconstruct the meaning from the rest of the page. Deferred cost exists.

7. Why the subject becomes structuring

Between two reviews, the initial value remains accessible: the sources converge on three terminals: 4 functions, 4 functions and article 4. They do not describe a universal average; they specify thresholds, obligations or operating conditions. In this case, the third source leads to the following operational reading: “Training must be proportionate to the tasks, decisions and people assigned. »

This reading transforms the figures into decision questions: what perimeter do they cover, what uncertainty remains and who can act when the measurement goes beyond the accepted threshold? On a register of usable models, this responsibility conditions the desired effect. This border matters.

8. Compare four levels of engagement

Concerning a usable model register, the comparison does not indicate a universal winner. It makes visible the cost of an absent proof, an overly simple driver or a premature extension. The right level depends on the criticality of the flow, the quality of the “evaluation game” and the concrete possibility of resuming the “change log”. The calendar serves as proof.

9. Recommended methodology: seven verifiable steps

Applied to a usable model register, the following method is good public and operational practice. It is not presented as a proprietary method of Logiks: its value comes from the order of controls and the possibility, for a third party, to verify each deliverable.

9.1. Formulating the decision

Here the action is to describe the expected result and relate it to “model identifier”. Run the check on a normal case and a degraded case, keeping the decision really open and the value that justifies it as a criterion. The concrete output takes the form of a memo from cadrage which names the decision, the limit and the person responsible.

9.2. Measuring the starting point

Faced with a deviation, the residual risk is accepted: this step transforms the intention into control: observing the decision indicator before any modification. Measure what actually changes in the starting situation and its variations between segments, including human recoveries. Document everything in an initial measure, dated and broken down by useful segment.

9.3. Trace Critical Path

To move forward without hiding the deferred cost, you need to link "score set" to the relevant data, teams, and dependencies. Compare before and after the exceptions encountered by the teams operating the system, then have a map of exceptions, dependencies and owners reread by an actor who did not design the test.

9.4. Laying down safeguards

Expected action: frame “deployment configuration” with limits, rights and a recovery procedure. Start on a perimeter where the team can still get back. The expected proof relates to limits, rights of action and the possibility of going back; record it in a control matrix that makes cost and reversibility visible.

9.5. Test the difficult case

The work consists first of testing “change log” in a representative scenario, then in a degraded scenario. Do not retain an ideal demonstration or an overall average: observe the nominal behavior, the failure caused and the quality of the recovery. The useful deliverable is an account of the nominal scenario, failure and human recovery.

9.6. Build evidence

On the critical path, the date of the source is checked: at this stage, it is necessary to compare results, errors, interventions and complete cost at the starting point. Involve the person handling the exceptions, then compare the outcome to the discrepancy between the initial promise and the recorded facts. You must be able to provide a file of logs, deviations and decisions that can be read by a third party to a decision maker who is absent from the project.

9.7. Decide and Review

Without a designated owner, the scope remains explicit: here, the action consists of assigning the review and tracking the measurement according to an explicit cadence. Run the check on a normal case and a degraded case, keeping the threshold that triggers a correction, extension, or shutdown as the criterion. The concrete output takes the form of a review rule with correction and stopping thresholds.

10. Logik tips: proof, mastery and reversibility

Our priority is the following risk: a list of API without traceability of actual changes. Start where this fragility already produces an expectation, a loss, or a contested decision; the prestigious perimeter can wait.

From the first test, the comparison maintains a previous state: keep the baseline measurement at the level where a team can act. A quarterly average does not replace an observation by course, by cohort or by type of exception; the marker must remain actionable.

Treat “model identifier” as a documented decision. A manager, a hypothesis, a limit and a review date are better than an adjustment whose origin no one knows.

Test "deployment configuration" with "change log" and then with a degraded recovery. The test should reveal operation and operating cost, not just confirm that the demonstration holds up.

Only extend the system if the observed facts support the desired effect and if the “evaluation game” remains controllable by a person outside the project.

In this file, the recommendations express a sequence judgment: make the risk observable, test the hypothesis relating to “deployment configuration”, then commit the resources. Sophistication comes after the demonstration of the announced effect; it does not replace it. The outing is prepared early.

11. Decision grid

The grid does not automatically produce arbitrage on a workable model register. On the other hand, it forces teams to show their assumptions about “model identifier”, their thresholds and their responsibilities; a disagreement is then explicit and can be resolved. This evidence is local.

12. Frequent errors

12.1. Consolidate activation and result

Enabling “model identifier” does not prove that the expected effect is achieved. This error shifts the debate towards the tool while the decision concerns an observable change.

12.2. Optimize the first available indicator

During the audit, human recovery is experienced: a convenient proxy can progress while the decisive measure deteriorates. Link each signal to a decision and a guardrail.

12.3. Ignore exceptions

Depending on the hypothesis adopted, a responsible function is named: the nominal path often hides the fragility described above. Test a borderline case, a failure and how the team regains control.

12.4. Leave an addiction without an owner

When “evaluation game” is everyone’s responsibility, no one decides the incident or the cost. Assign the decision before deployment.

12.5. Present risk as a formality

Documenting “deployment configuration” without patching the system produces facade compliance. The record must show a check performed and its result.

12.6. Extend without exit rule

If “change log” does not allow a decision, the pilot continues by inertia. Set continuation, correction and termination thresholds in advance.

13. Action Plan 30 / 60 / 90 days

13.1. Days 1 to 30: establishing the starting point

• describe the decision, the scope and the person responsible for it;
• record the initial value of the indicator before any modification;
• inventory dependencies and their exceptions;
• write the main risk and its detection condition.

In current operation, the measurement uncertainty remains visible: the first phase serves to make the disagreement visible. At thirty days, management must know the baseline measurement, the missing data and the specific case on which progress will be judged.

13.2. Days 31 to 60: testing the critical path

• implement primary control over a representative flow;
• test the recovery in a normal then degraded situation;
• record errors, human interventions, delays and costs;
• compare the observations to the initial scenario.

Under real constraints, external dependence is documented: this pilot does not only seek to demonstrate that the technology works. It must establish whether the system advances the selected indicator without shifting a disproportionate burden towards the operation, users or a supplier.

13.3. Days 61 to 90: decide and organize the continuation

• consolidate the evidence and have its limitations reread;
• assign each recurring control to a named function;
• confirm the next review date and discharge procedure;
• extend only if the facts support the effect initially announced.

In degraded mode, the decision to stop remains possible: at ninety days, the initial hypothesis must be demonstrated or refuted. Three decisions remain legitimate: extend, correct or stop the perimeter; continuing without a threshold does not constitute a fourth option.

14. FAQ

14.1. How to define a workable model registry?

It is a decision framework applied to an exploitable model registry. The approach links “model identifier” to “deployment configuration” and “change log” controls, with a baseline metric, owners, and an exit rule.

14.2. What to start with?

After an incident, the signal is broken down by segment: start with an actual decision, a baseline measurement and a previously observed manifestation of the main risk. The tool comes after this cadrage.

14.3. What budget should be retained?

At each check, the hypotheses remain rereadable: add up preparation, integration, operation, control, training, incidents and exit. Compare this full cost to the expected value, not just the license or campaign price.

14.4. How long should the test last?

The test must cover a full measurement cycle and at least one exception related to “deployment configuration”. Its duration derives from this observation, not from an arbitrary standard.

14.5. When to scale?

Scale up when progress remains stable, the 'change log' is controlled and responsibilities, costs and exit conditions are documented.

15. Conclusion

When the pilot is launched, the result keeps the same meaning: the decision is solid when a common measure links the technical, business and financial choices. The number of options activated is less important than the ability to explain discrepancies, deal with exceptions and reverse a choice that has become costly.

The pivot is simple: the project “a register of usable models” must no longer be a project to be delivered, but a capacity to govern to produce the announced effect. Reversibility decides.

16. Main sources

• NIST—AI Risk Management Framework — updated to 2026 — AI systems and services.
• NIST AI Resource Center — AI RMF — accessed 11 July 2026 — AI systems across industries.
• European Commission — AI Act article 4 — official text, consulted on July 11 2026 — personnel and service providers using AI systems in the European Union.
• OWASP GenAI — Prompt Injection — edition 2025, consulted on 11 July 2026 — LLM applications and connected agents.
• European Commission — AI Act article 14 — official text, accessed July 11 2026 — high-risk AI systems.