How Should AI Risks Be Covered by Internal Audit?

Artificial intelligence is becoming increasingly embedded in Saudi organizations, from financial analysis and customer service to cybersecurity, human resources, marketing, healthcare, and operational decision making. In this environment, consulting services internal audit can play an important role in evaluating whether AI systems are governed responsibly, protected against misuse, and aligned with organizational objectives. Saudi Arabia is also strengthening its position as an AI focused economy, with national initiatives supporting data and artificial intelligence development. The rapid expansion of AI infrastructure and adoption is making effective AI risk management increasingly important for organizations across the Kingdom.

For organizations operating in Saudi Arabia, AI risk should be considered across governance, data protection, cybersecurity, model performance, ethics, compliance, and business continuity. Insights consultancy can support organizations in understanding how internal audit can examine these risks through structured controls, testing procedures, risk assessments, and continuous monitoring. The importance of this approach is increasing as Saudi Arabia expands AI infrastructure and adoption. In 2026, major AI infrastructure investments in the Kingdom included plans involving an initial 100 MW data centre capacity in NEOM, demonstrating the scale of digital infrastructure being developed around AI.

Growing Importance of AI Risk Management in Saudi Arabia

Artificial intelligence introduces risks that differ from conventional information technology risks. Traditional technology audits often focus on access controls, system availability, cybersecurity, change management, and data protection. AI systems require these controls but also introduce additional concerns related to model accuracy, training data, algorithmic bias, explainability, automated decisions, model drift, hallucinations, and inappropriate outputs. Saudi Arabia’s national data and AI strategy is designed to position the Kingdom as a global leader in artificial intelligence by 2030. This strategic direction means organizations across financial services, energy, healthcare, government, telecommunications, retail, manufacturing, and professional services are likely to increase AI adoption. The expansion creates a need for internal audit functions to understand not only whether AI systems operate efficiently but also whether they operate within approved risk boundaries.

AI risk coverage should therefore consider:

• Governance and accountability
• Data quality and protection
• Cybersecurity
• Model reliability
• Privacy compliance
• Regulatory requirements
• Human oversight
• Third party AI providers
• Business continuity
• Ethical use of AI

AI Governance and Internal Audit Responsibilities

AI governance should establish clear responsibility for how artificial intelligence is selected, developed, deployed, monitored, and retired. Internal audit should assess whether the organization has established appropriate governance rather than becoming the owner of AI controls. A strong governance structure should identify who approves AI use cases, who owns individual models, who monitors performance, who manages incidents, and who has authority to suspend an AI system. Internal audit can evaluate whether the organization maintains a complete inventory of AI applications and whether each application has an assigned business owner and risk classification.

Important governance controls include:

• Formal AI policies
• Defined accountability for AI systems
• Approved use case registers
• Risk classification procedures
• Documented model ownership
• Periodic management reviews
• Escalation procedures
• Independent oversight
• Defined system retirement requirements

The audit function should also evaluate whether management has established thresholds for unacceptable AI behavior. Systems used for low risk administrative activities may require less intensive oversight than AI used for financial decisions, employee evaluation, customer eligibility, or healthcare related decisions.

AI Risk Assessment Framework

AI risk assessment should begin before an AI solution becomes operational. Internal audit can review whether management evaluates risks during the selection and implementation stages rather than waiting until an incident occurs. A practical assessment can examine the likelihood and potential impact of different AI risks. High impact AI applications should receive more frequent testing and stronger controls.

Risk assessment can consider:

• Nature of the AI decision
• Number of people affected
• Sensitivity of processed information
• Financial impact
• Regulatory exposure
• Cybersecurity exposure
• Potential reputational damage
• Degree of automation
• Dependence on external providers
• Ability to reverse automated decisions

The assessment should also consider how the AI system interacts with other technologies. An AI application connected to financial systems, customer databases, or enterprise applications can create greater exposure than a standalone content generation tool.

Data Quality and AI Model Reliability

AI systems are heavily dependent on data. Poor quality, incomplete, outdated, biased, or improperly sourced data can lead to unreliable outcomes. Internal audit should therefore evaluate data governance as part of AI risk coverage. Auditors can examine whether organizations know where training data originates, how data is processed, who can modify it, and whether appropriate quality controls exist.

Important data controls include:

• Data ownership
• Data classification
• Data validation
• Data lineage
• Access restrictions
• Data retention
• Data accuracy testing
• Sensitive data protection
• Monitoring of data changes

For Saudi organizations, data protection is particularly important because the Personal Data Protection Law creates obligations around personal data processing and protection. Organizations should ensure that AI applications process personal information according to applicable requirements and approved internal policies. Internal audit should assess whether AI applications process personal information appropriately and whether organizations have documented the legal and operational basis for such processing.

Generative AI and Hallucination Risks

Generative AI creates a specific risk because systems can produce information that appears convincing but may be inaccurate. This issue is commonly referred to as hallucination. For organizations, inaccurate AI generated information can result in financial mistakes, incorrect customer communications, flawed analysis, regulatory problems, or reputational damage. Internal audit should examine whether organizations have controls requiring human review when AI generated outputs influence important decisions.

Controls can include:

• Human approval for sensitive outputs
• Source verification procedures
• Accuracy testing
• Restricted use cases
• Output monitoring
• Escalation of uncertain responses
• Employee training
• Documentation of AI generated decisions

Organizations should establish clear procedures for verifying AI generated information before it is used in financial reporting, customer communications, regulatory submissions, or important business decisions.

Cybersecurity Risks Associated With AI

AI systems can increase cybersecurity exposure because they may process confidential information, connect to external services, use application programming interfaces, or interact with internal systems. An AI application may become a target for prompt manipulation, unauthorized access, data extraction, malicious inputs, or misuse of connected tools. Agent based AI systems can introduce additional concerns because they may perform actions rather than simply provide information. Internal audit should assess whether cybersecurity teams understand how AI applications interact with the organization’s wider technology environment.

Key areas include:

• Identity and access management
• API security
• Network controls
• Prompt security
• Data leakage prevention
• Monitoring and logging
• Vulnerability management
• Third party security
• Incident response

As AI systems become more capable of interacting with enterprise applications, internal audit should also evaluate whether automated actions are restricted according to defined permissions.

Privacy and Personal Data Protection

AI systems can process large volumes of personal information, including customer details, employee information, financial records, behavioural information, and other sensitive data. Internal audit should examine whether AI applications follow the organization’s data protection requirements and whether personal information is being used only for authorized purposes.

Audit procedures can assess whether:

• Personal data is properly classified
• Access is restricted
• Data retention periods are defined
• Third party processing is monitored
• Data transfers are controlled
• Privacy assessments are completed
• Employees understand AI data handling requirements

Organizations should also examine whether employees are entering confidential business information into publicly available AI tools without authorization. This can create significant data leakage risks even when the organization has not formally deployed the technology.

AI Bias and Fairness Risks

AI systems can reproduce or amplify biases contained in their training data or development processes. This can become especially important when AI is used for recruitment, credit decisions, customer segmentation, pricing, insurance, employee assessment, or other decisions affecting individuals. Internal audit should evaluate whether organizations have procedures for identifying and testing potential bias.

Testing can examine whether AI outcomes differ significantly across relevant groups and whether unusual patterns are investigated. Auditors should also determine whether management understands the limitations of the model and whether affected individuals have appropriate channels for review or escalation when automated decisions produce questionable results.

AI Model Validation and Performance Monitoring

AI models can change in performance after deployment. Changes in customer behaviour, market conditions, data quality, or business processes can cause model performance to decline. This creates the risk of model drift. Internal audit should assess whether management continuously monitors important AI systems rather than relying only on testing performed during implementation.

Performance monitoring can include:

• Accuracy rates
• Error rates
• False positive results
• False negative results
• Output consistency
• Data quality indicators
• Model drift
• User complaints
• AI incidents

The frequency of monitoring should depend on the risk classification of the system. High impact models may require continuous or frequent monitoring, while lower risk applications may be reviewed periodically.

Third Party AI Vendor Risks

Many organizations do not build AI models internally. Instead, they use external platforms, software providers, cloud services, or specialized AI vendors. This creates third party risk because organizations may have limited visibility into model development, training data, security controls, system changes, or subcontractors. Internal audit should review vendor due diligence and contractual controls.

Important considerations include:

• Vendor security assessments
• Data processing arrangements
• Confidentiality requirements
• Model transparency
• Service availability
• Incident notification
• Audit rights
• Business continuity
• Data deletion requirements
• Liability arrangements