AI Risk Assessment
AI Risk Assessment
AI Risk Assessment is a systematic approach to evaluating and mitigating risks associated with Artificial Intelligence models and applications. It ensures AI-driven systems remain secure, ethical, compliant, and aligned with organizational objectives.
AI Risk Assessment Process
AI risks are broadly categorized into the following areas:
Each risk type requires specific countermeasures:
AI Risk Assessment Tools & Frameworks
What We Offer
AI Risk Categorization & Control Measures
- Challenges: Overfitting, model drift, explainability.
- Controls: Implement AI Observability tools, use Explainable AI (XAI) techniques like SHAP & LIME, conduct model performance audits.
- Challenges: Data poisoning, bias, security leaks.
- Controls: Use differential privacy and homomorphic encryption, implement data versioning and lineage tracking, perform regular bias audits.
- Challenges: AI model hacking, adversarial attacks.
- Controls: Deploy AI-specific cybersecurity solutions, use robust encryption and API security, implement adversarial training techniques.
- Challenges: AI bias, non-compliance with AI laws.
- Controls: Align AI governance with NIST AI RMF, GDPR, EU AI Act, use AI Fairness 360 for bias detection, conduct regulatory impact assessments.
- Challenges: AI-driven misinformation, financial losses.
- Controls: Implement Human-in-the-loop (HITL) oversight, use fail-safe mechanisms for AI decision-making, conduct continuous AI stress testing.
Best Practices for AI Risk Management
Align AI strategy with enterprise risk management (ERM).
Secure ML models, APIs, and data pipelines.
Use explainability tools and model documentation.
Use bias testing, fairness validation, and security assessments.
GDPR, AI Act, ISO/IEC 42001, NIST AI RMF.
Define response workflows for AI failures.
Implement real-time AI observability tools.
