Use mapping
Tools, teams, data, decisions, outputs and dependencies.
DOSSIER 1101
Operational AI governance
Give teams freedom to use AI without making data, decisions and accountability invisible.
Operational briefPOLICYreadable rules
RISKclassified uses
HUMANclear accountability
Operational brief
Govern use, not only the tool.
Risk appears in real practices: copying data, delegating a decision, publishing without control or depending on a supplier. Governance must be understandable, testable and usable every day.
AR / DOSSIER 1101INDEPENDENT ANALYSIS
MEASUREMENT WORKSHOP
An agent with 80% success may pass only 64% of its programme
The denominator changes the interpretation. Of 50 planned trials, 40 finish and 32 pass: success among completed trials is 80%, but success across the whole programme is 64%. Keep the ten unfinished trials visible.
AI-generated illustration
Fictional example · no measurement of this site or a client
| Indicator | Calculation or threshold | What the result establishes |
|---|---|---|
| Programme completion | 40 / 50 × 100 = 80 % | 40 / 50 = 80%: ten trials still lack a completed outcome. |
| Success across all planned trials | 32 / 50 × 100 = 64 % | 32 / 50 = 64%: an unfinished trial is not a success. |
| Success among completed trials | 32 / 40 × 100 = 80 % | 32 / 40 = 80%: this conditional rate excludes ten unfinished trials. |
From observation to decision
Define success before testing
Write the expected outcome, allowed access, time limit and critical errors.
Keep every trial
Separate success, completed failure and non-completion. Critical failures are a subset of completed failures.
Review critical cases
Examine each incident and recovery path. A favourable average cannot authorize deployment.
CONTROLLED SYSTEM
Automation remains a responsibility.
Sources, permissions, evaluation, validation, logs and stopping conditions are designed with the system. Performance matters only while data, decisions and reversibility remain governable.
Illustrative scene.
02 / Intervention areas
Intervention areas
Risk classification
Impact, sensitivity, autonomy, recipients and correction potential.
Usage policy
Authorised, supervised and prohibited actions plus traceability duties.
Evaluation
Quality, robustness, bias, security, cost and out-of-scope behaviour.
Human control
Validation points, required skill and authority to stop.
Incident management
Detection, shutdown, evidence preservation, notification and correction.
03 / FIELD NOTES
When governance becomes necessary
As soon as use moves beyond individual experimentation and touches data, clients or decisions.
01SPREAD
Tools already everywhere
Teams use multiple assistants without a common framework or inventory.
02AUTOMATION
External actions
A system can publish, send, modify data or trigger a process.
03DATA
Sensitive information
Prompts or documents contain personal, strategic or contractual material.
04SUPPLIER
Growing dependency
An important function relies on a model, API or terms that may change.
04 / OUTPUTS
Governance framework
Short rules, a maintained register and concrete evaluation are better than an unusable charter.
Use register
Purpose, tool, owner, data, risk and status.
AI policy
Principles, allowed cases, prohibitions and validation duties.
Risk matrix
Common criteria and proportionate control levels.
Supplier sheet
Data, retention, jurisdiction, security, cost and reversibility.
Evaluation protocol
Test sets, thresholds, critical failures and frequency.
Incident plan
Shutdown, escalation, evidence, correction and lessons learned.
WORKING RECORDS
Make the dossier verifiable
Three practical records connecting diagnosis to a traceable decision.
Use-case register
Minimum content : For each use: purpose, owner, data, provider, affected people and any automated decision.
Check : Was a new use introduced without an owner?
Control map
Minimum content : Connect each risk to a safeguard, operating evidence, review frequency and repair owner.
Check : Does the control work in production or exist only in policy?
Incident record
Minimum content : Trace trigger, effects, stop decision, people notified, recovery and incorporated lesson.
Check : Can the event be reconstructed and a close recurrence prevented?
05 / Method
Usable governance
The framework is designed with users, tested on real cases and reviewed as tools or risks evolve.
- 01Inventory uses
- 02Name owners
- 03Classify data
- 04Assess risk
- 05Define validation
- 06Test systems
- 07Train users
- 08Review periodically
DECISION CHECKS
Decisions to document
Two checks that change the next decision.
Real use exceeds the inventory
Ask teams which data they submit, which decisions receive AI support and which tools are connected. Classify cases by potential impact before choosing controls.
An exception is requested
Record its reason, scope, owner and expiry date. Review it after an incident or model change; an indefinite exception bypasses the policy.
Two approaches to distinguish
Two approaches to distinguish
The right method depends on the question and the decision it must inform.
Use policy
Defines allowed data, tasks, tools and responsibilities. It provides a shared frame but does not alone show that real use follows the rules.
Operational control
Samples actual tasks, logs, exceptions and incidents. Checks permissions, output quality and human takeover; findings change the policy or tool.
Proposed work plan
Adapt this outline to the agreed scope. It describes a proposed method, not a completed mission, measured result or guaranteed schedule. JSON tracking fields are empty.
AI-generated illustration
Prepare before starting
Proposed uses, responsible actors, data and known vendors. Identify sensitive decisions and review mechanisms.
Scope the work
Inventory each use with purpose, owner and affected groups. Show dependencies and information still unknown.
Apply the method
Associate risks, checks and an approval owner. Prepare escalation and stopping rules before starting the system.
Verify and hand over
Review incidents, changes and check results on a defined schedule. Retain decisions and reasons for exceptions.
Pause or revision conditions
A use without an owner or checkable control remains unapproved. A governance register is not compliance certification.
06 / FAQ
Frequently asked questions
Clear answers on scope, limits, methods and engagement conditions.
Should public tools be banned?
Not systematically. Uses and data should be classified, with suitable alternatives.
Who owns governance?
Shared responsibility with one accountable decision-maker and business, technical and security owners.
Is a policy enough?
No. It needs inventory, controls, evaluation and an incident mechanism.
How are changing models managed?
Through regular tests, version monitoring and reassessment thresholds.
Does human review guarantee quality?
Only if the person has the time, skill and information needed.
Can personal data be used?
Depending on purpose, legal basis, contracts, security and applicable rules; specialist analysis may be needed.
How do you avoid blocking innovation?
Concentrate controls on risky uses and provide safe paths for experimentation.
Methodological references
Official documents explaining methods, review criteria and their limits. These references do not certify the service or guarantee search rankings.
08 / CONTACT
Open a private channel
State the context and objective. You will receive a clear initial reading.
DEEPER READING / PRACTICE
Attribute responsibilities around an AI use
An AI use case should be defined by its task, scope and the means of checking its outputs. Response quality and control over actions need to be assessed together.
Editorial supplement added on .
Starting situation
A tool is adopted by several teams without common rules. Governance begins with the inventory of uses, processed information and decisions that can be affected.
Priority checks
Define the owner of the use, user, validation manager and person responsible for the incidents. Document the data categories and the conditions of maintenance or stop.
Interpretation pitfalls
A general policy can remain abstract if it does not specify who controls the outputs. A human validation without sufficient information can become a simple formality.
Working record to retain
The usage register connects purpose, data, tool, responsibility and control. Incidents and scope changes trigger a system re-evaluation.
Apply the approach to an initial case
Start with a limited task whose entries and expected results can be examined. Provide incomplete information, a contradiction and an out-of-scope request. These cases show how the system deals with the unknown and when a person must resume the analysis or authorize an action.
Present the result and its limitations
Documenting the errors with the entrance and exit concerned. Before a new use, check if the correction concerns the order, data, permissions or human control. An evolution must be accompanied by a new observation of the affected cases so that it does not confuse confirmed adjustment and improvement.
PRACTICAL CASE / WORKED EXAMPLE
The IA release is validated without reading the sources
This pedagogical case examines AI use through its entries, permissions and controls. It does not describe a guaranteed performance; it helps to build an assessment adapted to the possible consequences of the task.
Example context
A team imposes human validation, but the manager does not have any documents used or the system’s limits. The agreement becomes a formality rather than a control.
Suggested approach
Defining the decision to be validated and the necessary information. To provide access to the sources and the useful context. Providing a status for cases that cannot be verified.
Conclusion and next step
The human role becomes a described and realisable operation. Uses whose output cannot be controlled are reviewed according to their scope and consequences.
Checklist for applying this example
- Relate permissions to the necessary actions to the authorized task.
- Test missing information, contradictions and requests out of scope.
- Define human times and results deemed acceptable.
- Keep the errors, versions and controls after modification.
Questions for deeper practice
What should a useful human validation contain?
A clear question, the elements to check and an expected result. The validator must be able to understand the exit, identify the limits and refuse or request a supplement when the evidence is missing.
How to decide if an AI system is ready for wider use?
Examine representative cases, major errors and conditions of recovery. The result must show what has been tested, with what criteria and what limits. An extension of task, data or permissions requires a review of risk and controls, even when the initial behavior was satisfying.
Why do I use the evaluation cases and control rules?
Inputs, tools and instructions can change. Keeping versions can explain why a behavior has changed and compare compatible executions. The cases added as a result of an error also document the situations now monitored, without making the boundaries of the previous evaluation game disappear.
DETAILED WORKSHOP / APPLICATION
Make validation responsibilities operational
This workshop offers a five-step sequence to apply the page labels. The entries, observations and conclusions are kept together in order to facilitate the verification and recovery of the work.
01 / Prepare the necessary information
Identify usages, outputs, responsible persons and affected decisions. Release the documents necessary for validation and situations where they are not available.
02 / Carry out the approach
Describe what each manager examines and can decide. Testing an ambiguous case, an error and a request for supplement. Verify that the validator has context and evidence.
03 / Examine an alternative situation
If validation consists only in confirming an incomprehensible output, review the check. A useful responsibility must correspond to a possible operation with sufficient information.
04 / Decide on the next step
Governance is exploitable when usages, permissions and incidents have identifiable responsibilities. scope changes have an explicit re-evaluation stage.
05 / Hand over the verification records
Transmit a registration of purpose, data, decision and control. The teams can understand their practical obligations without reconstructing the system from a general policy.
ANNOTATED WORKING DOCUMENT / REVIEW GRID
A register of uses with truly executable controls
The register combines use, data, decision, responsibility and validation. It describes the elements that the validator has and the conditions for maintenance, revision or stop.
On a small screen, scroll the grid horizontally to read all three columns.
| Observation | Supported interpretation | Next step |
|---|---|---|
| The manager can check the output and its sources. | The validation corresponds to a realisable and documented operation. | Try ambiguous cases and incidents and then keep decisions. |
| Validation is required without sufficient evidence. | Human responsibility is likely to become formal. | Identify the necessary information and recovery in case of uncertainty. |
| The data scope changes without review. | Previous controls do not necessarily cover new use. | Reexamine the permissions, responsibilities and assessment cases concerned. |
Reviewing the working document
The person responsible for verification must know who decides and with what information. A general policy can guide the system, but does not replace the control operations described for each use.
REPORTING / EXAMPLE WORDING
Presentation of results: Attribute responsibilities around an AI use
Point to clarify
A human validation must specify the elements that it actually allows to examine.
Before handing over
Verify that responsibility, access to evidence and decision-making power are consistent.
Example of a qualified conclusion
The controller has the information necessary for the described control. Cases that cannot be verified are ⁇ and follow the planned recovery.