Risk Management
While other areas like AI Governance and Ethics touch upon risk, comprehensive risk management dives deeper into the specific risks associated with AI implementation.
Key focus
Proactively identifying, assessing and mitigating potential AI-related risks.
It involves systematically identifying, assessing and mitigating these risks, which can range from technical challenges like model failures to operational risks like data breaches.
Other risks include ethical concerns, such as bias in algorithms, legal risks related to data privacy and compliance, and reputational risks that can arise from AI malfunctions or misuse.
Thorough risk identification is essential, covering all potential risks throughout the AI lifecycle. Each risk is assessed for its likelihood and impact, followed by mitigation strategies. Regular Monitoring and Evaluation are crucial to identify new risks and adapt mitigation plans accordingly.
Checklist
Five things to get right.
- 01Thoroughly identify all potential AI risks across the entire lifecycle
- 02Assess the likelihood and potential impact of each identified risk
- 03Develop and implement appropriate mitigation strategies for each risk
- 04Regularly monitor and evaluate AI systems for new or emerging risks
- 05Establish plans for responding to unforeseen risks
Maturity
What elevates it, and what diminishes it.
Elevates your maturity
- Proactive approach
- Risk awareness
- Robust mitigation
- Regular monitoring
- Preparedness plans
Diminishes your maturity
- Ignoring risks
- Underestimating impact
- Weak mitigation
- Lack of monitoring
- No backup plans
How mature is your Risk Management?
We help you assess where your organisation stands in Risk Management and the other 13 areas, and what to strengthen next for sustainable, measurable results.