AI Governance Is Shifting from Prohibition to Expectation Management: Future-Oriented AI Governance Enabled by the Trust Engine

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AI Governance Beyond Rules: How the Trust Engine Dynamically Controls AI Autonomy

Introduction

Generative AI is evolving from a tool that simply answers questions into an AI agent capable of assessing situations and taking action on its own.

AI creates documents.

AI responds to customers.

AI reviews contracts.

AI detects anomalies in equipment.

Multiple AI agents collaborate to carry out business processes.

As AI becomes increasingly involved in decision-making across business and society, AI governance becomes critically important.

Traditional AI governance has relied primarily on measures such as:

• Restricting the data AI can access
• Defining permitted operations through rules
• Prohibiting high-risk actions
• Requiring human approval
• Recording execution results in logs

These measures remain necessary.

However, governing autonomous AI solely through fixed rules has inherent limitations.

Real-world conditions are constantly changing.

Even when the same AI system is used, the level of risk varies depending on the task, user, data, timing, and potential social impact.

What is needed, therefore, is a mechanism that continuously evaluates how likely an AI system is to behave in a trustworthy manner in the future.

In this article, I call this mechanism the Trust Engine.

Trust Is Not a Judgment About the Past, but an Expectation About the Future

We usually think of trust as the result of past performance.

The system has consistently produced correct answers.

It has kept its promises.

It has not caused accidents.

It has received positive evaluations from many users.

Such records are important evidence when assessing trust.

However, the essence of trust does not lie in the past itself.

Trust is the expectation that:

“This person, organization, or AI system will continue to behave as expected in the future.”

Past performance is simply evidence used to calculate that expectation.

From this perspective, AI governance becomes more than a process of checking compliance with rules.

It becomes a mechanism for evaluating an AI system’s next action in terms of:

• The probability of producing the correct result
• The probability of complying with rules
• The probability of remaining aligned with its intended purpose
• The probability of explaining its actions if a problem occurs
• The probability of coordinating appropriately with humans and other AI systems
• The probability of operating reliably over time

The Trust Engine dynamically estimates this future trustworthiness.

Why Traditional Access Control Is Not Enough

In conventional information systems, access control has been at the center of governance.

Roles and permissions determine who can access which information and what operations they may perform.

For example:

• “Sales representatives may view customer information.”
• “Only administrators may delete data.”

For AI agents, however, having permission to use a capability is not the same as being able to use that capability appropriately.

Even an AI system authorized to access customer information may:

• Classify customers using insufficient evidence
• Make recommendations based on outdated information
• Perform actions that conflict with the customer’s intentions
• Use more personal information than necessary
• Send incorrect information to another system
• Make decisions that it cannot explain

AI governance must therefore consider more than:

“Does this AI have permission to perform this action?”

It must also ask:

“Should this AI be trusted to perform this action in this particular situation?”

The Trust Engine evaluates the expected reliability of delegating an action to an AI system, rather than merely checking whether permission exists.

What the Trust Engine Evaluates

Trust within a Trust Engine should not be determined by a single numerical score.

It must be evaluated through a combination of multiple factors.

1. Expected Capability

Does the AI possess the capabilities required to perform the task?

An AI system that performs well in general-purpose text generation may not have sufficient competence in specialized fields such as law, medicine, or industrial equipment control.

2. Expected Knowledge Adequacy

Does the AI have all the information required to make the decision?

The Trust Engine evaluates whether the AI has been provided with the latest regulations, internal rules, customer circumstances, past decision rationales, and other relevant context.

Even a highly capable model cannot make a trustworthy decision if the necessary knowledge does not reach it.

3. Expected Intent Alignment

Are the AI’s actions aligned with the objectives of humans and the organization?

An AI system may optimize the task immediately in front of it while deviating from the organization’s broader objectives or social values.

Local optimization does not always produce a desirable outcome for the system as a whole.

4. Expected Compliance

How likely is the AI to comply with laws, internal regulations, contractual conditions, ethical standards, and security policies?

The Trust Engine should do more than check whether an action matches a predefined prohibition. It must also evaluate whether changing circumstances have introduced new risks.

5. Expected Explainability

Will the AI be able to explain the basis of its decision afterward?

Critical decisions cannot be safely delegated if it is impossible to trace:

• What information was used
• Which alternatives were considered
• Why a particular action was selected

6. Expected Coordination

Can the AI collaborate appropriately with humans and other AI systems?

Trust also depends on whether the AI can understand the roles of other agents, resolve conflicting opinions, and escalate decisions to humans when necessary.

7. Magnitude of Impact

Even when the same AI system makes a decision, the required level of trust depends on the potential consequences of failure.

Summarizing an internal document is fundamentally different from:

• Entering into a contract
• Transferring funds
• Making a medical decision
• Shutting down industrial equipment

The Trust Engine evaluates not only the AI system itself, but also the potential impact of the action.

Adjusting AI Autonomy According to Trust

The Trust Engine does not simply permit or prohibit AI actions.

Its essential role is to adjust the level of autonomy according to the expected degree of trustworthiness.

The following levels provide one possible model.

Trust Level 1: Observation Only

The AI collects information and analyzes the situation, but does not take action.

Trust Level 2: Recommendation

The AI presents options or recommendations, while a human makes the final decision.

Trust Level 3: Approval-Based Execution

The AI creates an execution plan, but the plan must pass through a Human Gate before it can be carried out.

Trust Level 4: Limited Autonomous Execution

The AI may execute actions automatically as long as they remain within a predefined Boundary.

Trust Level 5: Continuous Autonomous Operation

Within domains where a high level of trust has been established, the AI continuously makes decisions and executes actions.

However, if the system detects an anomaly or a change in the environment, its level of autonomy is reduced.

The important point is that an AI system that has once achieved a high level of trust should not be granted permanent autonomy.

When circumstances change, expectations must also change.

For example:

• The AI begins handling a new type of data.
• Laws or internal regulations change.
• An unusual customer or situation appears.
• The behavior of another connected AI system changes.
• Failures or unexplainable decisions become more frequent.

When such changes occur, the Trust Engine reassesses trust and lowers the AI’s level of autonomy when necessary.

Trust Is a Dynamic State, Not a Fixed Score

The term “Trust Score” may suggest a system that assigns a permanent numerical rating to an AI system or person.

In reality, trust does not belong exclusively to the subject being evaluated.

Trust emerges from the relationship among:

• The AI system
• The task
• The situation
• The other parties involved
• The authority granted
• The associated risks

An AI system may be highly trustworthy when classifying internal documents but much less trustworthy when modifying contractual terms.

It may be allowed to operate autonomously under normal conditions but require human approval during a disaster or sudden market disruption.

The Trust Engine therefore does not ask the general question:

“Can this AI be trusted?”

A more accurate question is:

“If this AI performs this action for this purpose, within this context, what is the probability that it will achieve the expected result?”

Trust is not a fixed attribute. It is a dynamic state that changes within a relationship.

Decision Trace Provides the Evidence for Future Expectations

To evaluate future expectations, it is not enough to record past decisions merely as successes or failures.

We must also understand:

• Why the decision was made
• What the situation was at the time
• Which knowledge sources were consulted
• Which alternatives were available
• Which policies and constraints were applied
• Who approved the decision
• What outcome ultimately occurred

A Decision Trace records this information.

By accumulating Decision Traces, the Trust Engine can learn not only from accuracy rates, but also from the soundness of the decision-making process.

Even if the final outcome was successful, the AI should not receive a high level of trust if the success occurred merely by chance.

Conversely, even if the outcome was unsuccessful, the decision-making process may still deserve a positive evaluation if it was rational given the limited information available at the time.

The Trust Engine updates future expectations using not only results, but also the process leading to each decision and the consequences that followed.

Knowledge Flow Supports Trustworthy Decisions

The reliability of AI is not determined solely by the performance of the model.

The appropriate knowledge must reach the appropriate AI system at the appropriate time.

For example, a contract-review AI cannot make a correct decision if it does not know the latest contractual standards.

An AI system monitoring industrial equipment cannot properly assess an anomaly if it does not understand the equipment’s maintenance history or current production conditions.

Knowledge Flow supplies the knowledge required for decisions from sources such as:

• Enterprise documents
• Regulations and policies
• Historical records
• Ontologies
• Knowledge Graphs

The Trust Engine evaluates whether:

• All required knowledge is available
• The information is up to date
• The sources are trustworthy
• Different sources contradict one another
• Important contextual information is missing

Knowledge Flow delivers the information needed for a decision.

Decision Trace records the decision-making process.

The Trust Engine calculates the expected trustworthiness of delegating the next action.

Future AI Governance Will Operate as a Feedback Loop

AI governance centered on the Trust Engine operates through the following cycle:

Knowledge
    ↓
Context Understanding
    ↓
Decision
    ↓
Action
    ↓
Outcome
    ↓
Decision Trace
    ↓
Trust Update
    ↓
Autonomy Control

The AI receives knowledge, understands the current situation, makes a decision, and takes action.

The result is recorded as a Decision Trace.

The Trust Engine then uses that record to update expectations about future behavior.

The updated level of trust changes the autonomy, Boundaries, and Human Gate conditions applied to the AI’s next action.

As a result, AI governance is no longer a system in which rules are configured once during implementation and then left unchanged.

It evolves into dynamic governance that continuously learns from actual actions and outcomes.

Expectations Alone Are Not Enough

Expectation-based governance has enormous potential.

However, a high expectation of success must never mean that an AI system is allowed to do anything.

In areas involving life, physical safety, human rights, personal information, significant assets, or critical infrastructure, there must be Boundaries that cannot be crossed regardless of the Trust Score.

The Trust Engine is therefore not intended to eliminate fixed rules.

Future AI governance must combine the following mechanisms:

• Absolute Boundaries that must never be crossed
• Policies applied according to the situation
• Trust evaluations of future actions
• Human Gates that return important decisions to humans
• Decision Traces that record the decision-making process
• Knowledge Flows that deliver the necessary knowledge
• A Runtime Society that coordinates multiple AI systems

Within fixed safety Boundaries, the Trust Engine dynamically adjusts the AI’s level of autonomy.

This is the fundamental structure of AI governance that can achieve both flexibility and safety.

How the Trust Engine Will Transform Business and Society

Once the Trust Engine is implemented, organizations will no longer need to prohibit AI uniformly or require human confirmation for every operation.

Actions with high expected trustworthiness and limited impact can be delegated to AI.

Humans and AI can collaborate on decisions involving greater uncertainty.

Decisions with significant consequences can be routed through a Human Gate.

When anomalies are detected, autonomy can be reduced automatically.

This represents more than operational efficiency.

It is a social infrastructure through which society continuously determines how much responsibility can be delegated to AI according to actual circumstances.

In the future, collaboration will extend beyond AI systems within a single organization.

AI systems belonging to different companies, public institutions, communities, and individuals will interact and cooperate.

What will be needed is not a mechanism through which one organization centrally controls every AI system.

Instead, AI systems will need to verify one another’s:

• Purpose
• Authority
• Governing rules
• Record of trustworthiness
• Responsibility when problems occur

The Trust Engine will become a core component of the Trust Infrastructure that supports relationships between humans and AI, among AI systems, and between organizations and society.

Conclusion

AI governance is not merely a mechanism for restricting AI.

Its true purpose is to enable AI to participate safely in society and to create an environment in which humans and AI can collaborate continuously.

To achieve this, we must look beyond past performance and fixed rules and ask:

“If we delegate the next action to this AI, how likely is it that the desired future will be realized?”

The Trust Engine integrates past Decision Traces, current Context, available Knowledge, and potential future impact to evaluate Trust as an expectation about the future.

Based on that expectation, it dynamically adjusts the AI’s authority, autonomy, Boundaries, and Human Gate requirements.

The future of AI governance is not defined by simple permission and prohibition.

It is a continuous cycle:

Observe
    ↓
Evaluate
    ↓
Trust
    ↓
Act
    ↓
Learn

Trust is not a score awarded for the past.

It is the expectation that we can create a better future together.

What an AI-driven society needs is not the complete control of AI, but the cultivation of relationships in which responsibility can be safely delegated through a continuous cycle of expectations and outcomes.