Governance
The complex and rapidly evolving nature of AI makes them inherently difficult to govern effectively, leading to systemic regulatory and oversight failures.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1038
Domain lineage
6. Socioeconomic and Environmental
6.5 > Governance failure
Mitigation strategy
1. Establish cross-functional, executive-level AI governance bodies, such as a Chief AI Officer or a dedicated board risk committee, to integrate AI-specific risk management (e.g., based on the NIST AI Risk Management Framework) into the organization's enterprise-wide risk strategy, ensuring clear accountability across the entire AI lifecycle. 2. Mandate independent, third-party pre-deployment model audits and comprehensive risk assessments for all high-impact AI systems, granting auditors full access to test for safety, security vulnerabilities, and algorithmic bias to ensure compliance with predefined intolerable risk thresholds before operational deployment. 3. Implement continuous, automated AI observability platforms for real-time monitoring of model performance, data quality, and security indicators in the post-deployment phase to rapidly detect and mitigate risks such as model drift, bias amplification, or adversarial attacks, supported by defined human-in-the-loop oversight and incident response protocols.