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Artificial intelligence is changing how organizations in Saudi Arabia manage operations, customer services, finance, cybersecurity and decision making. As AI adoption expands, internal audit functions must also adapt to new risks involving data quality, algorithmic decisions, cybersecurity, privacy, access controls and third party technology. A consultant internal audit can help organizations evaluate whether AI systems are properly governed, whether controls remain effective and whether emerging technology risks are incorporated into the internal audit plan. This is particularly relevant for Saudi organizations advancing digital transformation under Vision 2030.
The growth of AI is creating a new risk environment for businesses across the Kingdom. Financial consultancy in Saudi Arabia increasingly needs to consider technology related risks alongside traditional financial and operational controls. The Communications, Space and Technology Commission reported that AI tool adoption in Saudi Arabia reached 45.2% in 2025, more than double the previous year. The same report recorded 99.6% internet penetration, demonstrating the scale of the Kingdom’s digital environment.
AI Adoption Is Changing the Internal Audit Landscape
Artificial intelligence can improve productivity, automate repetitive processes and support faster decision making. However, greater technology adoption also creates additional control requirements. Internal auditors must increasingly understand how AI systems receive data, process information, generate outputs and interact with business processes. AI can be embedded into several organizational activities, including:
- Financial forecasting
- Customer service
- Fraud detection
- Credit assessment
- Procurement
- Human resources
- Cybersecurity
- Marketing
- Document processing
- Risk management
- Business intelligence
- Regulatory monitoring
When these systems influence important business decisions, weaknesses in their design or governance can become control risks. An AI model may produce an incorrect result because of incomplete data, inappropriate assumptions or poorly designed processes. Internal audit therefore needs to examine not only the final output but also the controls surrounding the technology.
The Saudi Authority for Internal Auditors has also demonstrated the growing relevance of AI within the profession. Its AI tool Sara was launched to support internal auditors with analytical and informational capabilities and is hosted within Saudi Arabia to support data confidentiality and reliability.
AI Creates New Categories of Audit Risk
Traditional internal audit programs often focus on financial controls, operational processes, compliance and governance. AI introduces additional risk categories that may not have been included in older audit plans. These risks can include:
- Algorithmic errors
- Inaccurate training data
- Unauthorized AI usage
- Data leakage
- Model manipulation
- Weak access controls
- Inadequate human oversight
- AI generated misinformation
- Unclear accountability
- Third party AI dependencies
- Cybersecurity vulnerabilities
- Privacy violations
- Inadequate model documentation
The challenge for internal audit is that AI related risks can cross several departments at the same time. For example, an AI system used by finance may depend on information technology infrastructure, external software providers, customer data and financial databases. This means an isolated audit of the finance department may not identify the complete risk exposure.
The Growth of Generative AI
Generative AI has introduced another layer of complexity because employees can use AI tools without formal integration into enterprise systems. Employees may use publicly available AI applications to summarize documents, create reports, analyze information or generate business content.
The Saudi Internet Report 2025 recorded AI tool usage across several activities. Information search represented 80.8% of reported AI tool usage, while idea generation accounted for 40.1%. Work related tasks accounted for 17.3%.
From an internal audit perspective, this creates questions around shadow AI usage. An organization may have an approved AI platform while employees independently use other services. This can create uncertainty regarding:
- Where company data is being processed
- Who can access submitted information
- Whether sensitive information is retained
- Whether outputs are reviewed
- Whether AI usage complies with internal policies
- Whether third party providers meet security requirements
- Whether employees understand acceptable AI usage
Internal audit can assess whether the organization has sufficient governance over both officially approved and unofficial AI applications.
Data Quality Becomes a Critical Audit Issue
AI systems depend heavily on data. If input data is inaccurate, incomplete, outdated or biased, the resulting output may also be unreliable. This creates a fundamental internal audit question: can management rely on the data used by the AI system?
Data quality controls should cover:
- Data completeness
- Data accuracy
- Data consistency
- Data classification
- Data ownership
- Data access
- Data retention
- Data validation
- Data lineage
- Data security
A consultant internal audit can evaluate whether appropriate controls exist throughout the data lifecycle. This is especially important when AI is used for financial reporting, customer decisions, fraud detection or regulatory processes. The National Cybersecurity Authority has established Data Cybersecurity Controls designed to protect organizational data throughout its lifecycle and improve national cybersecurity maturity.
AI and Cybersecurity Risks
AI can improve cybersecurity monitoring, but it can also introduce new attack surfaces. Organizations using AI must consider how models, data, applications and external integrations could be exploited.
The National Cybersecurity Authority published a consultation on AI Cybersecurity Guidelines in July 2026. The proposed guidance covers AI cybersecurity governance, defense, resilience and third party cybersecurity, including emerging technologies such as generative AI and agentic AI.
This development demonstrates the growing importance of AI specific cybersecurity controls in Saudi Arabia. Internal audit can examine whether organizations have:
- AI cybersecurity policies
- Defined accountability
- Model access controls
- Secure application interfaces
- Data protection mechanisms
- Incident response procedures
- Third party security assessments
- Monitoring processes
- AI system inventories
- Regular control testing
The risk is not limited to cyberattacks. Poorly secured AI systems can also expose confidential business information or allow unauthorized users to manipulate outputs.
AI and Access Control Risks
Access control has traditionally been a major internal audit area. AI increases its importance because AI systems may connect to multiple databases, applications and information sources. If permissions are excessive, an AI application could potentially access information beyond what is required for its intended function.
Internal auditors should therefore assess:
- User access rights
- Privileged access
- Application permissions
- API access
- Service accounts
- Administrative privileges
- Segregation of duties
- Access review frequency
- Employee termination procedures
- Third party access
Organizations should establish clear rules regarding who can configure, modify, train, deploy and monitor AI systems.
Algorithmic Bias and Decision Risk
AI systems can influence decisions involving customers, employees, suppliers and investments. If the underlying data contains bias or the model is poorly designed, the resulting decisions may not be appropriate.
Internal audit does not necessarily need to redesign an AI model. Its role can include evaluating whether management has appropriate governance, documentation, testing and monitoring mechanisms. Relevant controls may include:
- Model validation
- Bias testing
- Independent review
- Documentation
- Human approval
- Exception monitoring
- Periodic reassessment
- Performance monitoring
- Defined escalation procedures
This is particularly important where AI outputs affect material financial or operational decisions.
AI Generated Information and Audit Evidence
Generative AI can produce convincing text even when the underlying information is inaccurate. This creates a challenge for internal audit because auditors may encounter AI generated reports, summaries and analyses during engagements.
Auditors should therefore establish procedures for verifying important information. Evidence should be assessed based on:
- Source reliability
- Data origin
- Documentation
- Reproducibility
- Independent verification
- Management approval
- System logs
- Supporting records
AI generated information should not automatically be treated as reliable audit evidence merely because it appears professionally written.
Third Party AI Providers
Many organizations do not develop AI systems internally. Instead, they use cloud platforms, software providers and specialized technology vendors. This creates third party risk. A Financial consultancy in Saudi Arabia may increasingly need to consider technology provider risks when supporting clients with financial control and governance assessments. Internal audit can evaluate whether management has properly assessed the provider before implementation.
Important considerations include:
- Vendor cybersecurity
- Data storage location
- Contractual protections
- Data ownership
- Service availability
- Business continuity
- Subcontractors
- Incident notification
- Access management
- Regulatory requirements
- Exit arrangements
Third party risk becomes particularly important when AI systems process financial information, customer information or confidential corporate data.
AI Risk in Financial Reporting
AI is increasingly used for forecasting, reconciliation, transaction analysis and financial planning. These applications can improve efficiency but also introduce new control considerations.
For example, an AI system could identify unusual transactions automatically. However, management still needs to understand how the system identifies anomalies and whether important transactions could be incorrectly classified.
Internal audit can examine:
- Data feeds
- Automated journal processes
- Reconciliation controls
- Exception handling
- Approval procedures
- Model changes
- Audit trails
- User access
- Output validation
- Management review
The objective is not to prevent automation. It is to ensure that automation operates within a controlled environment.
AI and Fraud Detection
AI can be used to identify unusual transaction patterns and potential fraud indicators. However, fraud detection systems can also produce false positives or fail to identify sophisticated activity.
Internal audit can assess whether AI based fraud monitoring has appropriate controls around:
- Alert generation
- Alert investigation
- Escalation
- Case documentation
- False positive monitoring
- Model performance
- Data completeness
- Employee access
- Management oversight
The effectiveness of an AI fraud system should be measured against defined risk indicators rather than assumed based solely on the use of advanced technology.
AI Governance and Accountability
One of the most important internal audit questions is who is responsible for an AI system. Without clearly assigned accountability, problems can remain unresolved because technology teams, business departments, risk functions and vendors may each assume that another party is responsible.
An effective governance framework can define:
- System ownership
- Data ownership
- Model ownership
- Approval authority
- Risk ownership
- Monitoring responsibility
- Change management responsibility
- Incident escalation
- Audit responsibility
- Vendor oversight
The National Cybersecurity Authority’s 2026 AI Cybersecurity Guidelines consultation identifies governance, defense, resilience and third party cybersecurity as core areas for managing AI related cybersecurity risks.
AI Risk and the Three Lines Model
Internal audit should maintain independence while working within the broader governance structure. Management remains responsible for designing and operating controls, while risk and compliance functions can provide oversight and monitoring. Internal audit provides independent assurance.
This distinction becomes particularly important with AI because internal auditors should not become responsible for operating the AI controls they later need to assess.
A structured model can involve:
- Management owning AI risks
- Technology teams operating technical controls
- Risk and compliance teams monitoring requirements
- Internal audit independently evaluating control effectiveness
This helps preserve audit independence while allowing internal audit to understand emerging technology risks.
AI Adoption Requires Continuous Monitoring
AI systems can change over time. Models may be updated, data sources may change and user behavior may evolve. A control that works effectively today may require reassessment after a major system change.
Internal audit should therefore consider continuous monitoring rather than relying entirely on annual reviews. Monitoring can cover:
- Model performance
- Error rates
- Access activity
- Data quality
- Security incidents
- Policy exceptions
- System changes
- Vendor changes
- User activity
- Regulatory developments
This approach allows organizations to identify control weaknesses earlier.
Saudi Arabia’s Digital Transformation Increases Audit Expectations
Saudi Arabia continues to invest heavily in digital technologies. The Digital Government Authority reported that government ICT spending reached SAR 31.90 billion in 2025, with government contracts worth approximately SAR 31.70 billion across more than 6,145 contracts. Spending on artificial intelligence and emerging technologies increased by 20%, while cloud computing spending increased by 42% compared with 2024.
The Digital Government Authority also reported an emerging technology adoption readiness score of 76.04% in 2026, compared with 74.69% in 2025, based on 54 participating government agencies.
These figures indicate the growing scale of technology adoption across the Kingdom. As organizations become more digitally dependent, internal audit functions need sufficient technology knowledge to assess related risks.
How Internal Audit Teams Can Adapt
Internal audit departments do not necessarily need to become technology development teams. However, they need sufficient knowledge to understand how AI affects organizational risks and controls.
Key capability areas include:
- AI governance
- Data analytics
- Cybersecurity
- Data privacy
- Model risk
- Technology controls
- Third party risk
- Automated controls
- Cloud technology
- Regulatory requirements
Audit teams can also use AI themselves to improve audit planning and analysis.
Gartner reported in August 2026 that 93% of surveyed audit leaders reported some level of AI use, while only 38% reported having an AI strategy. The survey covered 743 audit professionals. Gartner also reported that 30% were using AI for audit testing and 35% for risk assessment and audit planning.
These figures suggest that AI adoption within audit functions is already widespread, while strategic integration remains less developed.
AI Can Transform Audit Testing
Internal audit can use AI and analytics to examine large volumes of transactions rather than relying only on samples. Potential applications include:
- Duplicate payment detection
- Unusual transaction identification
- Journal entry analysis
- Access anomaly detection
- Procurement pattern analysis
- Expense monitoring
- Revenue analysis
- Vendor concentration analysis
- Fraud risk indicators
- Continuous control monitoring
However, automated audit testing requires its own controls. Auditors should understand how analytical rules operate, validate results and document methodology.
The Role of a Consultant in AI Related Internal Audit
A consultant internal audit can help organizations evaluate emerging AI risks when internal teams do not have sufficient technology, cybersecurity or data analytics expertise. External support can include risk assessment, control reviews, audit planning, governance assessment and evaluation of AI related processes.
The role can involve helping management identify where AI is being used across the organization and determining whether existing audit procedures adequately address those applications.
An effective review may examine:
- AI inventory
- AI governance
- Data management
- Cybersecurity controls
- Access management
- Vendor risk
- Model validation
- Human oversight
- Regulatory compliance
- Continuous monitoring
Building an AI Focused Internal Audit Plan
Organizations can gradually integrate AI risk into their existing internal audit methodology. The process can begin by identifying where AI is currently being used and classifying those applications according to their potential business impact.
A practical audit planning process can include:
- Identify all significant AI applications
- Classify systems according to risk
- Identify critical data sources
- Review governance arrangements
- Assess cybersecurity controls
- Evaluate access management
- Review third party dependencies
- Test model related controls
- Assess human oversight
- Monitor remediation activities
High impact AI applications should generally receive greater audit attention because errors could have material financial, operational, regulatory or reputational consequences.
AI Risk in the Saudi Regulatory Environment
Saudi organizations operate within an increasingly structured technology and cybersecurity environment. Depending on the sector, organizations may need to consider requirements from regulators and national authorities alongside internal policies.
For financial institutions, SAMA cybersecurity requirements provide a framework for managing cybersecurity risks and require periodic assessment of control effectiveness. The framework also identifies internal audit responsibility for performing cybersecurity audits.
For organizations handling sensitive data, national cybersecurity controls provide another important reference point.
Internal audit should therefore consider both technology risks and the applicable regulatory environment when designing AI focused audit procedures.
Future Direction of AI Driven Internal Audit in KSA
AI is increasingly integrated into both business operations and audit activities across Saudi Arabia. As adoption expands, internal audit needs to consider continuous monitoring, automated testing and technology enabled risk assessment.
The future internal audit environment may involve:
- Greater use of continuous auditing
- Automated transaction testing
- AI supported risk assessment
- Real time control monitoring
- Advanced fraud analytics
- Automated exception identification
- AI governance reviews
- Model risk assessments
- Increased third party technology oversight
- Greater integration between cybersecurity and internal audit
The increasing adoption of AI does not remove the need for professional judgment. Instead, it changes where that judgment is required. Auditors must understand whether automated systems are producing reliable results, whether controls are operating as designed and whether management has established sufficient accountability.
Strengthening Internal Audit for an AI Enabled KSA Economy
AI is reshaping the internal audit environment by introducing new risks around data, cybersecurity, algorithms, access, privacy, third party providers and automated decision making. At the same time, AI provides internal audit departments with opportunities to analyze larger data sets, improve testing efficiency and identify unusual patterns more quickly.
Saudi Arabia’s technology adoption figures demonstrate the scale of this transformation. AI tool adoption reached 45.2% in 2025, government emerging technology readiness reached 76.04% in 2026, and government ICT spending reached SAR 31.90 billion in 2025.
For Saudi organizations, the changing environment means internal audit needs to understand both traditional control risks and technology driven risks. A consultant internal audit can provide structured support in assessing AI governance, data controls, cybersecurity, model oversight, third party risks and audit readiness.
As organizations continue implementing artificial intelligence across finance, operations, customer services and decision making, internal audit can play an important role in providing independent assurance over whether AI is being used within an appropriately governed control environment. The combination of technology expertise, risk assessment, data analytics and traditional audit principles can help Saudi organizations respond to an increasingly AI enabled business environment.