1. Governance Challenges of Autonomous Agents
As generative AI evolves from information retrieval tools into autonomous agents capable of executing decisions, the risk boundaries of organizations are rapidly expanding.
When AI agents are able to allocate resources, approve transactions, or even participate in contractual decisions, traditional human-centric internal control frameworks must be fundamentally restructured.
In the 2026 regulatory landscape, the challenge is no longer about selecting the right technology. It is about demonstrating that algorithm-driven decisions are explainable, traceable, and compliant.
2. Risk Management Framework Based on ISO 42001 and NIST RMF
To establish a robust AI governance structure, internal auditors should focus on three core strategic areas:
Data Governance and Algorithmic Fairness
The audit focus has shifted from data integrity to identifying bias within datasets.
If training data contains hidden bias, automated decisions may expose the organization to significant legal and reputational risks.
Organizations should embed fairness testing into the development lifecycle and establish periodic bias review mechanisms.
Identity and Access Management for AI Agents
In an agent-based environment, each AI agent must be assigned a unique digital identity.
Auditors should ensure that:
- Access rights follow the principle of least privilege
- Human-in-the-loop controls are embedded at critical decision points
This prevents agents from operating beyond their authorized scope.
Explainability and Disclosure Requirements
With increasing global regulation, organizations must be able to explain how AI decisions are made.
By adopting Explainable AI techniques, auditors can help establish transparent decision pathways and ensure defensibility in the event of regulatory inquiries or legal disputes.
3. Practical Scenario: AI Agent Approving Vendor Payments
Consider a scenario where an organization deploys an AI agent to automatically approve vendor payments. The agent evaluates invoices based on historical transaction data, contract terms, and vendor ratings, and can trigger payment instructions without human intervention.
Key risks include:
-
Incorrect approvals
Failure to detect anomalies such as duplicate invoices or unusual amounts may result in financial loss -
Excessive access rights
Over-privileged agents may execute payments without proper control, violating segregation of duties -
Data bias risk
Biased or inaccurate historical data may lead to repeated poor decision-making -
Lack of audit trail
Without proper logging, decisions may not be explainable during disputes or audits
From an Internal Audit perspective, key controls should include:
- Threshold-based approvals requiring human review for high-risk transactions
- Comprehensive audit trails for all AI decisions
- Periodic model validation and bias testing
- Strict identity and access management aligned with segregation of duties
This scenario illustrates a fundamental shift: when AI becomes a decision-maker, internal controls must evolve accordingly.
4. Continuous Monitoring and Model Drift
Unlike traditional systems, AI models degrade over time as underlying data changes, a phenomenon known as model drift.
As a result, audit functions must evolve from periodic assessments to continuous auditing.
Organizations should implement automated Key Risk Indicators to monitor model performance. When deviations exceed predefined thresholds, escalation protocols and human review processes should be triggered.
5. Conclusion: Internal Auditors as Builders of Digital Trust
In an AI-driven business environment, the role of internal auditors extends beyond compliance assurance. We are becoming builders of digital trust.
By combining the rigor of financial auditing with the technical depth of information systems auditing, auditors can help organizations harness AI while maintaining transparency, resilience, and alignment with global standards.
The real challenge is no longer whether AI will make mistakes, but whether we are prepared to control and explain its decisions.
1. 自主代理帶來的治理挑戰
隨著生成式 AI 由資訊檢索工具演進為可自主執行決策的 AI Agents，企業的風險邊界正迅速擴張。
當 AI 代理具備調配資源、批核交易，甚至參與合約決策的能力時，傳統以人為核心的內部控制框架已無法完全應對，必須進行根本性重構。
在 2026 年的監管環境下，企業面對的關鍵不再是技術選型，而是如何證明其演算法決策具備可解釋性、可追溯性及合規性。
2. 基於 ISO 42001 與 NIST RMF 的風險管理框架
要建立穩健的 AI 治理體系，內部審計應重點關注以下三個核心領域：
數據治理與演算法公平性
審計重點已由數據完整性轉向識別數據偏見。
若訓練數據存在隱性偏差，將可能導致重大法律風險及商譽損失。
企業應在模型開發過程中嵌入公平性測試，並建立定期偏見審查機制。
AI 代理的身分與權限管理
在代理型 AI 環境中，每個 AI Agent 均應具備獨立數位身分。
審計人員需確保：
- 權限符合最小特權原則
- 在關鍵節點設置人工介入機制
以防止代理行為超出授權範圍。
演算法可解釋性與資訊披露
面對全球監管趨嚴，企業必須具備解釋 AI 決策邏輯的能力。
透過導入可解釋 AI 技術，審計師可協助建立透明決策流程，並在監管或法律情境下提供充分支持。
3. 實務案例：AI 自動批核供應商付款
假設企業部署 AI Agent 自動批核供應商付款。該代理根據歷史交易、合約條款及供應商評級進行判斷，並可直接觸發付款指令。
主要風險包括：
-
錯誤批核風險
未能識別重複發票或異常金額，可能導致資金損失 -
權限過大風險
過高權限可能違反職責分離，增加操作風險 -
數據偏差風險
錯誤或偏差數據可能導致持續錯誤決策 -
缺乏可追溯性
若無完整記錄，難以解釋決策原因
內部審計應建議以下控制措施：
- 設定付款門檻，高風險交易需人工覆核
- 建立完整 AI 決策日誌
- 定期進行模型驗證及偏差測試
- 強化身分與權限管理，確保職責分離
此案例說明，當 AI 成為決策執行者，內部控制設計必須同步升級。
4. 持續監控與模型漂移
AI 模型會因環境數據變化而產生效能下降，即模型漂移。
因此，內部審計需由定期審查轉為持續監控。
企業應建立自動化風險指標監測系統，當偏差超出範圍時，即時啟動應變及人工審核程序。
5. 結語：內部審計作為數位信任的構建者
在 AI 驅動的環境下，內部審計已由合規檢查者轉型為數位信任的構建者。
透過結合財務審計的嚴謹性與資訊系統審計的深度，審計人員可協助企業在提升效率的同時，建立透明且具韌性的治理體系。
真正的挑戰已不再是 AI 是否會出錯，而是當 AI 出錯時，我們是否有能力控制並解釋其決策。
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