The “AI Inventory” subject must lead to proof, not just to deployment: the expected effect must be measurable and reversible.
Frame the “purpose” point, control the “data” point, then decide with an explicit baseline measurement.
1. Key figures
| Number | What it establishes | Source, date and scope | Reading for you |
|---|---|---|---|
| 4 functions | The NIST GenAI profile complements Govern, Map, Measure, and Manage for risks specific to generative AI. | NIST — AI RMF Generative AI Profile, July 2024, accessed on 11 July 2026, generative AI systems across sectors | The usage inventory must link models, data, affected people and controls |
| 4 functions | The NIST AI RMF organizes risk management around Govern, Map, Measure and Manage. | NIST AI Resource Center — AI RMF, accessed on July 11 2026, AI systems across sectors | A register or assessment is only valuable if it triggers management decisions |
| item 4 | The AI Act requires suppliers and deployers to aim for a sufficient level of mastery of AI adapted to the people and context of use. | European Commission — AI Act article 4, official text, consulted on July 11 2026, personnel and service providers using AI systems in the European Union | Training must be proportionate to the tasks, decisions and people assigned |
| 2 injection channels | OWASP distinguishes between direct injection in the prompt and indirect injection carried by a file, a page or another external source. | OWASP GenAI — Prompt Injection, edition 2025, consulted on 11 July 2026, LLM applications and connected agents | The model should be treated as an unreliable interpreter and its powers limited in code |
| 5 human capabilities | Article 14 provides that human supervision makes it possible in particular to understand, monitor, interpret, ignore or reverse, and interrupt the system. | European Commission — AI Act article 14, official text, accessed on July 11 2026, high-risk AI systems | A human in the loop is only useful if he has information, skills, authority and a real means of stopping |
These benchmarks limit the decision on a risk-oriented register of AI systems; 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 calendar serves as proof.
After going into production, the budget limit is noted: for this subject, the first source leads to the following operational reading: “The inventory of uses must link models, data, affected people and controls. » 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
Between two reviews, the hypothesis can be contradicted: a source is useful when a reader simultaneously understands what it asserts, 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 risk-oriented AI systems”, 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 “purpose” 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 RMF Generative AI Profile provides the indication “4 functions” here. This information informs a choice; it does not, by itself, demonstrate that the same effect will appear in your context. The outing is prepared early.
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. This evidence is local.
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. Reversibility decides.
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. The test must stand.
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. This benchmark does not decide.
3. Reusable citation sheet
Without a designated owner, the rights of action are documented: a robust citation must be able to be reproduced 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.
| Field | Content to keep |
|---|---|
| Verifiable assertion | The NIST GenAI profile complements Govern, Map, Measure, and Manage for risks specific to generative AI. |
| Attribution | NIST — AI RMF Generative AI Profile, July 2024, accessed 11 July 2026 |
| Declared scope | generative AI systems across all sectors |
| Value or bound | 4 functions |
| Operational reading | The usage inventory must link models, data, affected people and controls. |
| Decision concerned | Connect “finality” to a local observation before arbitrage |
| Magazine owner | Trades — Avoid digitizing unquestioned friction |
| Condition of revision | Reexamine the citation if the source, scope, or “vendor and version” changes |
4. Introduction: framework the primary risk
The subject seems technical until the first contested arbitrage. The points “purpose”, “data”, “decisional impact” and “supplier and version” nevertheless belong to the same decision path.
The concrete risk takes the following form: a list of tools that ignores integrated automations and individual uses. 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.
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. The context requires the proof.
5. Actors and responsibilities
| Actor | Responsibility in the decision | Point of vigilance |
|---|---|---|
| Professions | Describe the actual work, exceptions, and value | Avoid Scanning Unquestioned Friction |
| AI and data team | Designs data, evaluations, models and observability | Measure the complete task and failure cases |
| DSI and security | Manages identities, tools, risks and continuity | Limit scope, secrets and irreversible actions |
| Template Providers | Provide capabilities, limits and developments | Monitor costs, versions, retention and dependencies |
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 answer depends on the cycle.
6. Definition: Risk-Oriented AI Systems Registry
In this guide, the scope “a register of risk-oriented AI systems” combines the points “purpose”, “data”, “decisional impact” and “provider and version”. The objective is to obtain known uses with owners, affected persons and controls; the decision is based on the coverage of active uses by a revised classification.
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. Exceptions reveal maturity.
7. Why the subject becomes structuring
Because the context evolves, the calculation unit does not change: 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 risk-oriented level of AI systems, this responsibility conditions the desired effect. The risk is concrete.
8. Compare four levels of engagement
| Level | What it optimizes | Decision criterion | Limit to make visible |
|---|---|---|---|
| Observation without reference measurement | Apparent speed | purpose | The result cannot be attributed |
| Narrow-minded pilot | Learning on a flow | Deviation from reference measurement | The tested case may remain too simple |
| Governed deployment | Demonstrated effect on the useful perimeter | “Decisional impact” and “supplier and version” controls | The recurring cost must remain explicit |
| Reduction or cessation | Control of the main risk | Documented exit threshold | Preserve data, evidence and reversibility |
When it comes to a risk-oriented registry of AI systems, the comparison does not point to 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 “data” and the concrete possibility of resuming “supplier and version”. The threshold remains explicit.
9. Recommended methodology: seven verifiable steps
Applied to a risk-oriented register of AI systems, 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
The work consists first of describing the expected result and linking it to “purpose”. Do not retain an ideal demonstration or an overall average: observe the truly open decision and the value that justifies it. The useful deliverable is a memo cadrage which names the decision, the limit and the person responsible.
9.2. Measuring the starting point
At the next milestone, operations can resume: at this stage, the decision indicator must be observed before any modification. Involve the person who handles the exceptions, then compare the result to the initial situation and its variations between segments. You must be able to provide an initial measurement, dated and broken down by useful segment, to a decision-maker absent from the project.
9.3. Trace Critical Path
The action here is to connect “data” to the relevant data, teams, and dependencies. Run the check on a normal case and a degraded case, keeping the exceptions encountered by the teams operating the device as a criterion. The concrete output takes the form of a map of exceptions, dependencies and owners.
9.4. Laying down safeguards
This step transforms intention into control: framing “decisional impact” with limits, rights and a recovery procedure. Measure what actually changes in boundaries, action rights, and rollback ability, including human takeovers. Document everything in a control matrix that makes cost and reversibility visible.
9.5. Test the difficult case
To move forward without hiding the deferred cost, you must experience "vendor and version" in a representative scenario, then in a degraded scenario. Compare before and after on the nominal behavior, the failure caused and the quality of the recovery, then have an account of the nominal scenario, the failure and the human recovery reread by an actor who did not design the test.
9.6. Build evidence
If the measurement diverges, the measurement date is recorded: expected action: compare result, errors, interventions and full cost at the starting point. Start on a perimeter where the team can still get back. The expected proof relates to the discrepancy between the initial promise and the recorded facts; record it in a file of logs, deviations and decisions that can be read by a third party.
9.7. Decide and Review
As long as doubt remains, the full cost becomes apparent: the work consists of first assigning the review and tracking the measurement according to an explicit cadence. Do not retain an ideal demonstration or an overall average: observe the threshold that triggers a correction, an extension or a stop. The useful deliverable is a review rule with correction and stopping thresholds.
10. Logik tips: proof, mastery and reversibility
Our priority is the following risk: a list of tools that ignores integrated automations and individual uses. Start where this fragility already produces an expectation, a loss, or a contested decision; the prestigious perimeter can wait.
During the audit, the threshold has an owner: keep the baseline metric 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 “finality” as a documented decision. A manager, a hypothesis, a limit and a review date are better than an adjustment whose origin no one knows.
Experience “decision impact” with “vendor and version” and then with 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 “data” 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 “decisional impact”, then commit the resources. Sophistication comes after the demonstration of the announced effect; it does not replace it. The average can deceive.
11. Decision grid
| State | Signal observed | Expected proof | Cautious decision |
|---|---|---|---|
| To frame | “purpose” exists without named result | dated reference measurement | Do not engage the entire perimeter |
| As a pilot | “data” is tested on a real flow | Deviation from starting point | Include a representative exception |
| Governed | “decisional impact” has a manager and a review | Stability, cost and incidents | Document degraded mode |
| To expand or stop | “vendor and version” allows a decision | Net worth and residual risk | Apply exit rule |
The grid does not automatically produce arbitrage on a risk-oriented AI systems registry. On the other hand, it forces teams to show their assumptions about “purpose”, their thresholds and their responsibilities; a disagreement is then explicit and can be resolved. The perimeter is authentic.
12. Frequent errors
12.1. Consolidate activation and result
Activating “purpose” 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
With incomplete data, local verification can be reproduced: a convenient proxy can progress while the decisive measurement deteriorates. Link each signal to a decision and a guardrail.
12.3. Ignore exceptions
When faced with a deviation, exceptions are logged: the nominal route 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 “data” 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 “decisional impact” without correcting the system produces facade compliance. The record must show a check performed and its result.
12.6. Extend without exit rule
If “vendor and version” 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.
On the critical path, the stopping rule is known: 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.
On the business side, the trace remains auditable: 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.
When the pilot is launched, the initial value remains accessible: 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 risk-oriented AI systems register?
This is a decision framework applied to a risk-oriented AI systems registry. The approach links “purpose” to “decisional impact” and “supplier and version” controls, with a reference measurement, those responsible and an exit rule.
14.2. What to start with?
Depending on the assumption made, the sample remains representative: start with a real decision, a reference measurement and an already observed manifestation of the main risk. The tool comes after this cadrage.
14.3. What budget should be retained?
In current operation, the convincing element remains linked to the decision: add 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 complete cycle of the measurement and at least one exception linked to “decisional impact”. Its duration derives from this observation, not from an arbitrary standard.
14.5. When to scale?
Scale up when progress remains stable, “vendor and version” is controlled, and responsibilities, costs, and exit conditions are documented.
15. Conclusion
Outside of the nominal scenario, the next deadline is planned: 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 risk-oriented AI systems” must no longer be a project to be delivered, but a capacity to govern to produce the announced effect. The compromise appears clearly.
16. Main sources
- NIST — AI RMF Generative AI Profile — July 2024, accessed on 11 July 2026 — generative AI systems across all sectors.
- 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.