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CORAA यूनिवर्सिटी · टूल· विद्यालय

When your क्लाइंट uses AI, what should you ऑडिट?

Map AI जोखिम to SA 315 जोखिम-मूल्यांकन प्रक्रियाएँ. 15 प्रश्न across 5 categories, model, डेटा, operational, अनुपालन, वित्तीय विवरण impact.

क्लाइंट AI profile
जोखिम मूल्यांकन, 15 प्रश्न
Rate each जोखिम factor: 1 (Low जोखिम) to 5 (High जोखिम)
Model Risk
औसत 3/5
1. Is the AI model a black box (unexplainable)?3/5
Low जोखिमHigh जोखिम
2. How frequently is the model retrained/updated?3/5
Low जोखिमHigh जोखिम
3. Has the model been independently validated?3/5
Low जोखिमHigh जोखिम
Data Risk
औसत 3/5
1. Is the training data biased or unrepresentative?3/5
Low जोखिमHigh जोखिम
2. Are data inputs validated before feeding the model?3/5
Low जोखिमHigh जोखिम
3. Is there proper data governance (lineage, quality, access)?3/5
Low जोखिमHigh जोखिम
Operational Risk
औसत 3/5
1. Is there human oversight of AI decisions?3/5
Low जोखिमHigh जोखिम
2. Are there fallback procedures if the AI system fails?3/5
Low जोखिमHigh जोखिम
3. Is there monitoring for model drift or degradation?3/5
Low जोखिमHigh जोखिम
Compliance & Regulatory Risk
औसत 3/5
1. Does the AI system comply with applicable regulations (RBI, SEBI, etc.)?3/5
Low जोखिमHigh जोखिम
2. Is there documentation of the AI system's design and operation?3/5
Low जोखिमHigh जोखिम
3. Are there ethical guidelines for AI use?3/5
Low जोखिमHigh जोखिम
Financial Statement Impact
औसत 3/5
1. Could AI errors materially affect financial statements?3/5
Low जोखिमHigh जोखिम
2. Are AI-derived estimates (provisions, valuations) material?3/5
Low जोखिमHigh जोखिम
3. Is the AI system part of the client's internal controls?3/5
Low जोखिमHigh जोखिम
Overall जोखिम score
3
5.0 में से
जोखिम rating
High
High-जोखिम categories
0
जोखिम heat map
Model Risk3/5 · High
Data Risk3/5 · High
Operational Risk3/5 · High
Compliance & Regulatory Risk3/5 · High
Financial Statement Impact3/5 · High
निम्न (≤2)मध्यम (2–3.5)उच्च (>3.5)
Recommended ऑडिट प्रक्रियाएँ
Model RiskHigh जोखिम
  • Test model outputs against manual calculations
  • Review model validation reports and methodology
  • Assess explainability documentation and audit trail
Data RiskHigh जोखिम
  • Test input controls and data validation procedures
  • Verify data completeness and representativeness
  • Review data governance framework and access controls
Operational RiskHigh जोखिम
  • Test override controls and human review procedures
  • Review incident logs and business continuity plans
  • Evaluate model monitoring and alerting mechanisms
Compliance & Regulatory RiskHigh जोखिम
  • Review regulatory filings and compliance certifications
  • Test compliance monitoring procedures
  • Assess ethical AI policy and governance framework
Financial Statement ImpactHigh जोखिम
  • Perform substantive procedures on AI-generated amounts
  • Test reasonableness of AI-derived accounting estimates
  • Evaluate design and implementation of AI-related controls
SA 315 जोखिम-मूल्यांकन mapping
Model Risk
Understand the entity's AI models as part of the information system (SA 315.18-19)
Data Risk
Evaluate IT general controls over data integrity (SA 315.21)
Operational Risk
Assess control activities over AI operations (SA 315.26)
Compliance & Regulatory Risk
Identify regulatory compliance risks affecting financial reporting (SA 315.11)
Financial Statement Impact
Identify and assess risks of material misstatement from AI systems (SA 315.25-30)
मुख्य अंतर्दृष्टि, AI प्रणालियों का ऑडिट
High जोखिम. Significantly expand substantive प्रक्रियाएँ. Consider engएजिंग an IT-ऑडिट specialist. Test AI outputs against independent calculations.
ऑडिट AI-powered क्लाइंट

AI का ऑडिट करने में मदद चाहिए? - स्वचालित परीक्षण और दस्तावेज़ीकरण।

CORAA helps ऑडिटर navigate AI जोखिम with continuous monitoring and SA 315 mapped प्रक्रियाएँ.

अगला

Run defensible प्रक्रियाएँ across the एंगेजमेंट.

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