What Is AI Governance?

AI governance is the system of responsibilities, policies, processes and controls used to direct and oversee the development, procurement and use of artificial intelligence.

A complete AI governance framework may address:

1. AI      strategy

2. Risk      appetite

3. Acceptable      use

4. AI      inventory

5. Risk      classification

6. Impact      assessments

7. Data      governance

8. Development      and procurement

9. Testing      and validation

10. Human      oversight

11. Implementation      approval

12. Performance      monitoring

13. Incident      management

14. Change      management

15. Record      keeping

16. Independent      assurance

17. Retirement      and decommissioning

AI governance should cover systems that are:

• Developed      internally

• Purchased      from vendors

• Embedded      in third-party platforms

• Used      experimentally

• Accessed      directly by employees

• Used      to support decisions

• Used      to make automated decisions

• Integrated      with other systems

• Used      by agents or service providers

An organization should not limit its AI framework to systems formally described as AI.

Models, algorithms or automated tools may create comparable risks even where a vendor uses different terminology.

AI Risk Management

AI risk management is the process of identifying, assessing, controlling and monitoring risks arising from artificial intelligence.

The National Institute of Standards and Technology AI Risk Management Framework is designed to help organizations incorporate trustworthiness considerations into the design, development, use and evaluation of AI systems. Its core functions are commonly described as Govern, Map, Measure and Manage.

An AI risk assessment may consider:

• Intended      purpose

• Affected      individuals

• Decision      significance

• Level      of autonomy

• Data      sensitivity

• Data      quality

• Accuracy

• Reliability

• Explainability

• Fairness

• Security

• Privacy

• Legal      requirements

• Customer      impact

• Operational      dependency

• Vendor      dependency

• Reversibility

• Human      oversight

• Potential      misuse

• Potential      scale of harm

The assessment should distinguish between:

• Inherent      risk

• Existing      controls

• Control      effectiveness

• Residual      risk

• Risk      acceptance

• Required      remediation

AI risk assessments should be updated when:

• The      model changes.

• The      use case changes.

• New      data is introduced.

• The      affected population changes.

• Performance      deteriorates.

• A      significant incident occurs.

• Regulation      changes.

• The      vendor changes its service.

• The      system becomes more autonomous.

AI Governance in Financial Services

Financial services organizations already operate within extensive regulatory frameworks.

The introduction of AI does not remove existing obligations involving:

• Governance

• Accountability

• Consumer      protection

• Fair      treatment

• Privacy

• Financial      crime

• Operational      resilience

• Outsourcing

• Model      risk

• Cybersecurity

• Regulatory      reporting

• Record      keeping

The UK Financial Conduct Authority has stated that its existing rules apply to firms’ use of AI, including requirements connected with accountability, governance and Consumer Duty. Its approach seeks to support responsible adoption rather than treat AI as separate from the wider regulatory framework.

The FCA’s July 2026 Mills Review also examined how AI may reshape retail financial services and emphasized both the potential benefits and risks for consumers, firms, markets and regulators.

For regulated organizations, AI governance should therefore be integrated into existing:

• Enterprise      risk management

• Operational      risk management

• Compliance

• Model      risk management

• Privacy

• Information      security

• Vendor      risk management

• Product      governance

• Internal      audit

• Board      reporting

Creating an entirely separate AI framework without connecting it to established governance may create duplicated responsibilities and inconsistent controls.

AI Governance in Canada

Canada’s AI regulatory environment continues to develop.

The federal government introduced the proposed Artificial Intelligence and Data Act as part of Bill C-27 in 2022. Government materials describing that proposal have since been archived, while Canada announced a broader National Artificial Intelligence Strategy in June 2026 and introduced new federal privacy legislation through Bill C-36. Organizations should therefore verify the current legislative position rather than relying on outdated descriptions of AIDA as enacted law.

Even where a comprehensive AI statute does not yet apply, Canadian organizations may remain subject to requirements arising through:

• Privacy      legislation

• Human-rights      law

• Consumer-protection      law

• Contract      law

• Employment      law

• Financial      services regulation

• Sector-specific      requirements

• Regulatory      guidance

• Common-law      duties

Federally regulated financial institutions should also consider OSFI’s model-risk expectations.

OSFI’s final Guideline E-23 on Model Risk Management is scheduled to take effect in May 2027 and expressly recognizes that AI and machine-learning models can intensify model risk, including through dynamic learning and autonomous decision-making.

OSFI and the Financial Consumer Agency of Canada have also identified AI risks involving areas such as explainability, data, cybersecurity, third parties, legal risk and consumer outcomes.

Organizations outside direct OSFI supervision may still use these materials as useful reference points when designing proportionate AI governance.

AI Governance in the United States

The United States applies a combination of federal, state and sector-specific requirements to AI.

Organizations may need to consider laws involving:

• Consumer      protection

• Fair      lending

• Equal      opportunity

• Privacy

• Credit      reporting

• Employment

• Unfair      or deceptive practices

• State      automated-decision requirements

• Cybersecurity

• Intellectual      property

Federal agencies have emphasized that existing legal requirements continue to apply when decisions are made or supported by automated systems.

A joint statement from U.S. enforcement agencies addressed the use of automated systems and discrimination, while the Federal Trade Commission has repeatedly stated that AI does not create an exemption from existing consumer-protection law.

The FTC has also highlighted concerns involving inaccurate systems, bias, privacy, deceptive practices and AI-enabled fraud.

Organizations operating in the United States should assess both:

• The      law governing the underlying decision

• Any      additional requirements governing automated or AI-assisted processing

AI governance should be adaptable because state requirements can evolve independently and may differ in scope, terminology and effective dates.

AI Governance in the United Kingdom

The United Kingdom has generally pursued a regulator-led approach under which existing regulators apply their current mandates to AI.

For financial services firms, relevant requirements may arise through:

• FCA      Principles

• Consumer      Duty

• Senior-management      accountability

• Operational      resilience

• Outsourcing

• Financial      crime rules

• Data      protection

• Model      governance

• Customer      communications

• Product      governance

The FCA has emphasized that firms remain responsible for the outcomes produced through AI and should not attribute accountability to the technology itself.

The Information Commissioner’s Office provides detailed guidance on applying UK data-protection principles to AI systems. Its guidance addresses fairness, transparency, accountability, individual rights and automated decision-making.

The ICO also provides practical guidance on explaining AI-assisted decisions to affected individuals and a toolkit for identifying data-protection risks across the AI lifecycle.

European Union AI Act Considerations

Organizations operating in or serving individuals in the European Union may need to assess the EU Artificial Intelligence Act.

The Act uses a risk-based framework and imposes different requirements depending on how an AI system is classified and used.

Potential categories include:

• Prohibited      practices

• High-risk      AI systems

• Systems      subject to transparency requirements

• General-purpose      AI models

• Lower-risk      uses

An organization may be affected as a:

• Provider

• Deployer

• Importer

• Distributor

• Product      manufacturer

• Authorized      representative

The assessment should consider not only where the organization is located but also where the AI system is placed on the market, used or produces relevant effects.

Because the Act has staged implementation dates and detailed classification rules, organizations should maintain a documented applicability analysis and implementation roadmap rather than assume that every AI use case is treated in the same way.

AI Strategy

AI governance should begin with a clear organizational strategy.

The strategy may define:

• Why      the organization wants to use AI

• Which      business outcomes it expects

• Which      uses are encouraged

• Which      uses require enhanced oversight

• Which      uses are prohibited

• How      AI aligns with customer and regulatory obligations

• Who      is accountable for implementation

• How      value and risk will be measured

A strategy can help prevent the organization from adopting AI simply because the technology is available.

AI investments should have a clear purpose, measurable benefit and defined risk owner.

AI Risk Appetite

AI risk appetite establishes the boundaries within which the organization is prepared to use AI.

It may address:

• Automated      customer decisions

• Customer-facing      generative AI

• Use      of confidential data

• Use      of personal information

• Unapproved      public AI tools

• Autonomous      financial transactions

• Employee      monitoring

• Biometric      processing

• High-impact      decisions

• Regulatory      submissions

• Legal      advice

• Code      generation

• Model      explainability

• Vendor      concentration

• Human      oversight

The organization may prohibit certain activities entirely.

Other activities may be permitted only when:

• A      risk assessment is complete.

• Legal      and compliance review has occurred.

• Human      approval is maintained.

• Customer      disclosure is provided.

• Testing      thresholds are satisfied.

• Senior-management      authorization is obtained.

Risk appetite should be translated into operational rules rather than remain a high-level statement.

Acceptable-Use Policy

An AI acceptable-use policy defines how employees, contractors and other authorized users may use AI tools.

The policy may address:

• Approved      platforms

• Prohibited      platforms

• Confidential      information

• Personal      information

• Customer      data

• Intellectual      property

• Regulatory      information

• Source      verification

• Human      review

• Record      keeping

• Software      code

• External      communications

• Customer      decisions

• Regulatory      filings

• Incident      reporting

Employees should understand that information entered into an AI tool may be processed, stored or used in ways that differ from ordinary internal software.

The policy should clearly state which categories of information must not be entered into public or unapproved AI systems.

AI Inventory

An organization cannot govern AI systems it does not know it uses.

The AI inventory should include:

• System      name

• Business      owner

• Technical      owner

• Vendor

• Purpose

• Users

• Affected      customers

• Data      used

• Output

• Decision      impact

• Level      of autonomy

• Integrations

• Risk      classification

• Approval      status

• Validation      date

• Monitoring      frequency

• Contract      details

• Retirement      date

The inventory should include:

• Formal      production systems

• Pilot      projects

• Embedded      vendor functionality

• Employee      productivity tools

• Models      developed by data teams

• Generative      AI assistants

• Automated      agents

• Free      or trial services

Shadow AI may emerge when employees use publicly available tools without formal approval.

Periodic surveys, technology monitoring and vendor reviews can help identify unauthorized use.

AI Risk Classification

Not every AI system requires the same level of control.

An internal classification methodology may consider:

• Customer      impact

• Financial      impact

• Regulatory      significance

• Degree      of automation

• Data      sensitivity

• Number      of individuals affected

• Reversibility

• Explainability

• Use      of biometrics

• Vulnerable      customers

• Safety      implications

• Vendor      dependency

A possible classification structure might include:

Low Risk

Examples may include:

• Internal      grammar assistance

• Basic      document formatting

• Non-sensitive      meeting summaries

• Internal      research support

Moderate Risk

Examples may include:

• Marketing-content      generation

• Internal      workflow prioritization

• Employee      productivity analysis

• Non-binding      customer-service suggestions

High Risk

Examples may include:

• Customer      risk scoring

• Fraud      detection

• Transaction-monitoring      prioritization

• Credit      decisions

• Account      restrictions

• Employee      decisions

• Regulatory      reporting

• Customer-facing      financial guidance

Prohibited or Restricted

Examples may include:

• Unauthorized      use of customer data

• Fully      autonomous adverse customer decisions without required review

• Deceptive      impersonation

• Unapproved      biometric surveillance

• Generation      of false regulatory evidence

The classification should determine the level of approval, testing, monitoring and documentation required.

AI Impact Assessments

An AI impact assessment evaluates the potential effects of a proposed system before implementation.

It may address:

• Intended      purpose

• Legal      basis

• Customer      impact

• Affected      groups

• Data      sources

• Privacy

• Fairness

• Accuracy

• Explainability

• Cybersecurity

• Operational      resilience

• Human      oversight

• Vendor      risk

• Misuse

• Appeals

• Monitoring

• Incident      response

The assessment should identify both intended and reasonably foreseeable unintended uses.

A customer-service assistant, for example, might initially be approved only to provide general information but later begin influencing complaint decisions or product recommendations.

The organization should prevent material expansion of the use case without additional review.

Data Governance

AI systems depend on data.

Weak data can produce inaccurate, discriminatory or unreliable outcomes.

AI data governance should consider:

• Data      ownership

• Data      provenance

• Collection      authority

• Lawful      processing

• Consent      where required

• Quality

• Completeness

• Accuracy

• Representativeness

• Labelling

• Retention

• Security

• Access

• Bias

• Data      drift

• Synthetic      data

• Training      data

• Testing      data

The organization should know:

• Where      the data came from

• Whether      it may be used for the proposed purpose

• Whether      it contains personal information

• Whether      it reflects the population affected

• Whether      it has known gaps

• Whether      it changes over time

Large volumes of data do not automatically create reliable results.

Poor-quality data at scale can produce poor-quality decisions at scale.

Privacy and AI

AI systems may collect, infer, combine or generate information about individuals.

Privacy risks may arise during:

• Data      collection

• Model      training

• Fine-tuning

• Prompt      processing

• Output      generation

• Monitoring

• Retention

• Vendor      access

• Cross-border      transfers

The privacy assessment should consider:

• Legal      authority

• Purpose      limitation

• Data      minimization

• Transparency

• Accuracy

• Retention

• Individual      rights

• Automated      decision-making

• Security

• Vendor      processing

• Secondary      use

A system may generate sensitive inferences even where the original data was not classified as sensitive.

For example, AI may infer information about:

• Financial      distress

• Health

• Vulnerability

• Behaviour

• Relationships

• Location

• Risk      propensity

Privacy impact assessments and AI impact assessments should be coordinated rather than completed in isolation.

Fairness, Bias and Discrimination

AI systems can produce unequal outcomes because of:

• Biased      training data

• Incomplete      data

• Historical      discrimination

• Proxy      variables

• Poor      feature selection

• Inappropriate      thresholds

• Unequal      error rates

• Feedback      loops

• Inadequate      testing

• Changes      in the affected population

Fairness testing may consider:

• Approval      rates

• Denial      rates

• False      positives

• False      negatives

• Error      distribution

• Customer      complaints

• Override      patterns

• Outcomes      by relevant group

• Accessibility

• Language      performance

Removing a protected characteristic from the data does not necessarily eliminate discrimination.

Other variables may act as proxies.

Fairness should be assessed in the context of the actual outcome rather than solely through mathematical performance.

Accuracy and Reliability

AI outputs can be incomplete, inconsistent or incorrect.

Generative AI systems may produce information that appears authoritative but is unsupported or false.

Accuracy controls may include:

• Benchmark      testing

• Golden      datasets

• Independent      validation

• Source      verification

• Confidence      thresholds

• Human      review

• Output      restrictions

• Retrieval      from approved information

• Citation      requirements

• Error      testing

• Adversarial      testing

The required accuracy level should reflect the consequence of error.

A minor wording error in an internal draft may be acceptable.

An incorrect sanctions decision, customer-risk classification or regulatory filing may not be.

Explainability and Transparency

Organizations should be able to explain:

• What      the AI system does

• Why      it is used

• What      data influences the result

• How      the output affects decisions

• What      limitations exist

• Whether      human review occurs

• How      an individual can challenge the outcome

The level of explanation should reflect:

• The      audience

• The      decision

• The      potential harm

• The      complexity of the model

• Legal      requirements

• Security      considerations

Explainability does not always require disclosure of proprietary source code.

It generally requires enough meaningful information for the organization, reviewer or affected individual to understand the basis and significance of the decision.

A system should not be used for a material decision simply because no one can explain it.

Human Oversight

Human oversight should be meaningful rather than ceremonial.

A human reviewer should have:

• Appropriate      knowledge

• Sufficient      time

• Access      to relevant information

• Authority      to disagree

• Ability      to override the output

• Clear      escalation routes

• Training

• Accountability

Automation bias can occur when employees accept AI outputs because the system appears objective or technically sophisticated.

Controls should encourage reviewers to challenge outputs and document material overrides.

The organization should also monitor whether humans actually exercise independent judgment or merely approve the system’s recommendation.

Automated Decision-Making

AI may support or make decisions involving:

• Credit

• Pricing

• Customer      onboarding

• Fraud

• Account      restrictions

• Claims

• Complaints

• Employment

• Marketing

• Customer      risk

The organization should determine whether the system:

• Provides      information

• Recommends      a decision

• Makes      the initial decision

• Makes      the final decision

• Executes      the decision automatically

This distinction affects:

• Legal      analysis

• Customer      disclosure

• Human      oversight

• Appeals

• Explainability

• Testing

• Governance

A nominal human review may not be meaningful where staff routinely follow the AI recommendation without analysis.

Generative AI

Generative AI can create:

• Text

• Images

• Audio

• Video

• Software      code

• Summaries

• Recommendations

• Customer      communications

Key risks include:

• Hallucinated      information

• Confidential-data      exposure

• Infringement      of intellectual property

• Biased      content

• Inconsistent      outputs

• Prompt      injection

• Insecure      code

• Misleading      customer communications

• Unclear      source material

• Excessive      reliance

Controls may include:

• Approved      tools

• Restricted      data inputs

• System      prompts

• Retrieval      from validated sources

• Output      filtering

• Human      review

• Logging

• User      training

• Citation      checks

• Security      testing

• Use-case      restrictions

Employees should not treat generated content as verified merely because it is written confidently.

Agentic AI and Autonomous Systems

Agentic AI can plan and perform multiple actions toward an objective.

An AI agent may be able to:

• Access      systems

• Retrieve      information

• Send      communications

• Create      records

• Update      customer files

• Execute      transactions

• Call      external tools

• Delegate      tasks to other agents

These capabilities can create heightened risk because the system is not only generating information—it is taking action.

Agentic AI governance should address:

• Permitted      actions

• Identity      and access

• Transaction      limits

• Human      approval points

• Tool      permissions

• Data      boundaries

• Monitoring

• Audit      trails

• Kill      switches

• Error      recovery

• Escalation

• Segregation      of duties

The principle of least privilege should apply.

An AI agent should have access only to the systems and actions required for its approved purpose.

High-impact or irreversible actions should generally require additional authorization.

Model Development

Internally developed AI systems should follow a controlled development lifecycle.

The lifecycle may include:

• Business      requirements

• Risk      classification

• Data      approval

• Design      documentation

• Development      standards

• Testing

• Validation

• Security      review

• Privacy      review

• Compliance      review

• User      acceptance testing

• Implementation      approval

• Monitoring

• Change      management

• Retirement

Development documentation should allow another qualified person to understand:

• The      objective

• The      methodology

• The      data

• The      assumptions

• The      limitations

• The      testing

• The      expected performance

• The      monitoring requirements

Rapid experimentation should not bypass controls when the system begins to influence real customers or regulated decisions.

Model Validation

Validation should be proportionate to the risk and complexity of the system.

It may examine:

• Conceptual      soundness

• Data      quality

• Methodology

• Performance

• Fairness

• Stability

• Explainability

• Security

• Limitations

• Implementation

• Documentation

• Monitoring      thresholds

The validator should have sufficient independence from the developer.

For higher-risk systems, validation may include:

• Out-of-sample      testing

• Stress      testing

• Sensitivity      analysis

• Benchmark      comparison

• Adversarial      testing

• Outcome      analysis

• Back      testing

• Scenario      testing

Validation should occur before implementation and periodically thereafter.

AI Performance Monitoring

AI systems can deteriorate over time.

Performance changes may arise from:

• Data      drift

• Concept      drift

• Customer      behaviour

• Economic      change

• Fraud      adaptation

• Product      changes

• Regulatory      changes

• Vendor      updates

• System      integrations

• Feedback      loops

Monitoring may include:

• Accuracy

• Error      rates

• False      positives

• False      negatives

• Fairness

• Override      rates

• Customer      complaints

• Processing      failures

• Latency

• Uptime

• Data      quality

• Output      consistency

• Unexpected      behaviour

Thresholds should trigger:

• Investigation

• Recalibration

• Additional      human review

• Restricted      use

• Temporary      suspension

• Revalidation

• Retirement

Monitoring should assess outcomes, not merely technical system availability.

AI Change Management

Changes to an AI system may materially alter its risk.

Changes may involve:

• New      training data

• New      prompts

• New      model versions

• New      thresholds

• New      integrations

• New      users

• Expanded      autonomy

• New      customer groups

• New      jurisdictions

• Vendor      updates

Change procedures should define:

• Materiality

• Required      testing

• Required      approvals

• Documentation

• Rollback      arrangements

• Customer      communication

• Regulatory      assessment

• Post-implementation      monitoring

Vendor-provided systems can change without traditional internal software releases.

Contracts and oversight processes should therefore address notification of material model changes.

AI Vendor Risk

Many organizations use AI through third-party providers rather than developing systems internally.

Vendor due diligence may assess:

• Corporate      stability

• Technical      capability

• Data      use

• Data      retention

• Model      training

• Security

• Privacy

• Intellectual      property

• Subprocessors

• Model      limitations

• Bias      testing

• Validation

• Explainability

• Service      continuity

• Incident      history

• Regulatory      status

• Insurance

• Exit      arrangements

The organization should understand:

• Whether      its data is used to train the model

• Where      data is processed

• Which      subcontractors are involved

• Whether      the vendor can change the model

• Whether      the customer can access logs

• How      incidents are reported

• How      data is deleted

• What      happens when the contract ends

A vendor’s assurance report or certification may support due diligence but should not replace assessment of the specific use case.

AI Concentration Risk

AI markets may be concentrated among a limited number of:

• Foundation-model      providers

• Cloud      providers

• Data      providers

• Semiconductor      providers

• Specialized      vendors

Multiple internal systems may depend on the same underlying model or infrastructure without management realizing the degree of concentration.

The organization should identify:

• Common      providers

• Common      infrastructure

• Common      datasets

• Geographic      concentration

• Subcontractor      concentration

• Exit      barriers

• Replacement      time

• Interoperability

• Vendor      lock-in

Concentration should be assessed across the enterprise rather than one contract at a time.

Cybersecurity and AI

AI can create new cybersecurity exposures and can also be used to strengthen security.

Relevant risks may include:

• Prompt      injection

• Data      poisoning

• Model      theft

• Adversarial      inputs

• Credential      exposure

• Insecure      integrations

• Unauthorized      tool calls

• Malicious      code generation

• Confidential-data      leakage

• Manipulation      of AI agents

• Compromised      training data

Security controls may include:

• Access      restrictions

• Input      validation

• Output      filtering

• Network      segregation

• Secure      APIs

• Authentication

• Encryption

• Monitoring

• Red-team      testing

• Rate      limits

• Tool      restrictions

• Incident      response

OSFI has highlighted emerging technology, cybersecurity and operational-resilience concerns connected with advanced and frontier AI systems.

AI and Financial Crime

AI can strengthen:

• Fraud      detection

• Sanctions      screening

• Transaction      monitoring

• Customer      risk rating

• Adverse-media      review

• Investigations

It can also introduce risks involving:

• Missed      alerts

• Excessive      false positives

• Unexplained      risk scores

• Biased      customer treatment

• Hallucinated      adverse information

• Weak      record keeping

• Regulatory-reporting      errors

Financial crime decisions should be supported by reliable evidence.

An AI-generated summary should not be treated as the source itself.

Where AI supports suspicious-transaction analysis, sanctions decisions or customer exits, the organization should retain sufficient records to explain:

• The      information reviewed

• The      system output

• Human      analysis

• Final      decision

• Escalation

• Reporting

AI and Fraud Risk

AI can be used by fraudsters to create:

• Deepfakes

• Synthetic      identities

• Phishing      messages

• Impersonation      scams

• Fraudulent      documents

• Malicious      code

• Automated      account attacks

Organizations may also use AI to detect unusual behaviour, linked identities and payment patterns.

AI fraud controls should be governed using the same standards applied to other high-impact systems.

A sophisticated detection model can still create harm if it incorrectly blocks legitimate customers or produces unexplained account restrictions.

AI and Consumer Protection

AI systems can influence customer outcomes through:

• Product      recommendations

• Pricing

• Eligibility

• Communications

• Fraud      controls

• Complaint      decisions

• Customer      support

• Marketing

The organization should assess whether customers receive:

• Accurate      information

• Fair      outcomes

• Appropriate      explanations

• Accessible      service

• Human      assistance

• Effective      challenge routes

• Protection      from foreseeable harm

Customer-facing AI should clearly distinguish itself from a human where confusion could be material.

The system should also avoid giving individualized financial, legal or regulatory advice unless that function has been approved and controlled appropriately.

Customer Complaints and Appeals

Customers should have a practical route to challenge significant AI-assisted decisions.

The process may include:

• Clear      notification

• Explanation

• Human      review

• Additional      evidence

• Correction      of inaccurate data

• Appeal

• Complaint      escalation

• Outcome      communication

Complaint information can identify:

• Systematic      errors

• Unfair      outcomes

• Poor      explanations

• Accessibility      issues

• Vendor      problems

• Emerging      customer harm

AI-related complaints should be included in performance monitoring and governance reporting.

AI Incident Management

An AI incident may involve:

• Materially      inaccurate outputs

• Discriminatory      outcomes

• Unauthorized      decisions

• Data      leakage

• Security      compromise

• Model      manipulation

• System      instability

• Vendor      failure

• Regulatory      breach

• Customer      harm

• Unauthorized      AI use

The incident process should address:

1. Detection

2. Containment

3. Customer      protection

4. System      suspension where necessary

5. Severity      assessment

6. Legal      and regulatory analysis

7. Root-cause      investigation

8. Communication

9. Remediation

10. Validation

11. Lessons      learned

The organization should define when an AI issue also constitutes:

• A      privacy breach

• A      cybersecurity incident

• An      operational incident

• A      regulatory breach

• A      customer-harm event

• A      vendor incident

AI Business Continuity and Resilience

Organizations may become dependent on AI systems for critical activities.

Resilience planning should consider:

• Provider      outage

• Model      withdrawal

• API      failure

• Excessive      latency

• Degraded      performance

• Data-source      failure

• Security      incident

• Vendor      insolvency

• Regulatory      restriction

• Loss      of specialist staff

Continuity arrangements may include:

• Manual      alternatives

• Secondary      providers

• Previous      model versions

• Controlled      fallback rules

• Data      backups

• Export      capability

• Emergency      access

• Customer      communication

The organization should know whether an important service can continue safely without the AI system.

AI Record Keeping

AI records should allow the organization to reconstruct:

• Why      the system was approved

• Which      version was used

• What      data was processed

• Which      output was produced

• Who      reviewed it

• Which      decision was made

• Which      controls operated

• Which      changes occurred

• Which      incidents were identified

Records may include:

• Risk      assessments

• Impact      assessments

• Validation      reports

• Approval      decisions

• Prompts

• Outputs

• Logs

• Overrides

• Model      versions

• Training      records

• Monitoring      results

• Customer      notices

• Vendor      documents

• Incident      records

Record-keeping requirements should be proportionate.

Not every employee prompt requires permanent retention, but high-impact decisions should maintain sufficient evidence for review, complaint handling and regulatory examination.

AI Training and Awareness

AI training should reflect employees’ responsibilities.

General training may cover:

• Approved      tools

• Prohibited      data

• Hallucinations

• Source      verification

• Confidentiality

• Privacy

• Intellectual      property

• Security

• Incident      reporting

Specialist training may be required for:

• Developers

• Validators

• Compliance

• Risk      management

• Privacy

• Cybersecurity

• Procurement

• Internal      audit

• Customer      service

• Senior      management

Employees should understand both the capability and limitations of AI.

Training should discourage blind reliance while supporting informed use.

AI Governance Structure

AI governance may be overseen through:

• The      board

• A      risk committee

• A      technology committee

• An      AI governance committee

• A      model-risk committee

• Existing      product-approval governance

Responsibilities may include:

Board

• Approving      strategy and risk appetite

• Overseeing      material AI risks

• Challenging      significant use cases

• Reviewing      material incidents

Senior Management

• Implementing      the framework

• Assigning      resources

• Approving      high-risk systems

• Ensuring      cross-functional coordination

Business Owners

• Defining      purpose

• Assessing      customer impact

• Operating      controls

• Monitoring      outcomes

Technology and Data Teams

• Development

• Documentation

• Testing

• Security

• Technical      monitoring

Risk and Compliance

• Independent      challenge

• Regulatory      analysis

• Risk      assessment

• Monitoring

Privacy and Legal

• Data      use

• Transparency

• Rights

• Contracts

• Legal      risk

Internal Audit

• Independent      assurance over governance and control effectiveness

The organization should avoid creating a committee without clearly assigning decision authority and accountability.

AI Key Risk Indicators

AI key risk indicators may include:

• Number      of AI systems

• Number      of unauthorized systems

• High-risk      use cases

• Overdue      assessments

• Validation      exceptions

• Accuracy      rates

• False      positives

• False      negatives

• Bias      indicators

• Human      override rates

• Customer      complaints

• Data-quality      incidents

• Security      events

• Vendor      outages

• Unresolved      findings

• Model      changes

• AI-related      regulatory breaches

Indicators should include thresholds and escalation requirements.

An increase in overrides may indicate that employees are appropriately challenging the system—or that the system is performing poorly.

Context is therefore essential.

Management and Board Reporting

AI reporting should help management understand:

• Where      AI is used

• Which      systems create the greatest risk

• Whether      controls are effective

• Whether      outcomes remain fair and accurate

• Whether      material incidents have occurred

• Whether      remediation is progressing

• Whether      vendor dependency is increasing

• Whether      the organization remains within risk appetite

Board reporting should focus on:

• Material      risks

• Strategic      dependencies

• High-impact      customer uses

• Significant      incidents

• Regulatory      developments

• Concentration      risk

• Resource      needs

• Unresolved      weaknesses

Reporting should not focus only on innovation benefits or the number of AI projects.

Compliance Monitoring and Testing

AI controls should be included in the compliance monitoring plan.

Testing may assess:

• Inventory      completeness

• Risk      classification

• Impact      assessments

• Approval

• Data      use

• Customer      disclosure

• Human      oversight

• Output      accuracy

• Fairness

• Vendor      controls

• Incident      reporting

• Record      keeping

• Employee      compliance

Testing should review actual outputs and decisions rather than confirming only that a policy exists.

Monitoring results should feed into:

• Risk      assessments

• Model      validation

• Product      governance

• Training

• Remediation

• Board      reporting

Independent Assurance

Independent assurance may assess whether the AI framework is:

• Appropriately      designed

• Implemented

• Operating      effectively

• Proportionate

• Integrated      with other risk disciplines

• Supported      by adequate records

• Adapted      to regulatory change

An independent review may cover:

• Governance

• Inventory

• Risk      classification

• Data

• Models

• Vendors

• Human      oversight

• Customer      outcomes

• Incidents

• Monitoring

• Training

• Reporting

Independent assurance should not rely exclusively on technical performance.

It should also assess governance, compliance and operational outcomes.