Managing AI Risk
Managing AI Risk
With the increased use of AI and the inevitable broader adoption of this technology
effective risk management and oversight of AI systems will require a comprehensive and multi-faceted approach that integrates governance, regulatory compliance, risk assessment, operational controls, and a strong organizational culture. By addressing these areas, organisations can leverage AI’s potential while safeguarding against its risks.
Hagan Smiths AI Risk Framework (AIRF) enables Risk management oversight of an
AI system to ensure that the deployment of such technology aligns with
organisational strategy and goals and regulatory requirements while minimising
potential adverse impacts. Our key considerations for effective oversight are:
Governance and Accountability
- Board and Executive Involvement: Ensure the board and senior executives are aware of AI initiatives and their implications. Establish clear lines of accountability for AI systems.
- Ethical Standards: Develop and enforce ethical guidelines for AI development and use, addressing issues such as bias, fairness, and transparency.
Regulatory Compliance
- Stay Informed: Continuously monitor regulatory developments related to AI in relevant jurisdictions. Adapt policies and practices to remain compliant.
- Data Privacy: Implement robust data governance frameworks to protect personal and sensitive data used by AI systems.
Risk Identification and Assessment
- Risk Categories: Identify and categorise risks associated with AI, including operational, strategic, reputational, and compliance risks.
- Scenario Analysis: Conduct scenario analysis to anticipate potential failures or adverse outcomes from AI deployment.
Model Risk Management
- Model Validation: Establish rigorous processes for the validation and periodic revalidation of AI models. Ensure models are tested for accuracy, reliability, and robustness.
- Explainability: Prioritize the development of explainable AI systems to facilitate understanding and trust among stakeholders.
Operational Controls
- Monitoring and Reporting: Implement continuous monitoring of AI systems to detect and respond to anomalies. Develop reporting mechanisms to keep stakeholders informed of AI performance and risks.
- Incident Management: Create incident response plans specifically for AI-related failures or breaches, ensuring swift and effective mitigation.
Third-Party Risk Management
- Vendor Assessment: Thoroughly vet third-party AI providers and their technologies. Assess the security, ethical practices, and compliance of their offerings.
- Ongoing Oversight: Maintain oversight over third-party AI systems and ensure they adhere to the organization’s risk management standards.
Skill Development and Culture
- Training Programs: Develop training programs to enhance the AI literacy of employees, particularly those involved in risk management and oversight.
- Cultural Alignment: Foster a culture that values risk awareness and ethical considerations in AI development and deployment.
Technological Safeguards
- Robust Infrastructure: Ensure that the technical infrastructure supporting AI systems is secure, resilient, and capable of handling the computational demands.
- Security Measures: Implement advanced security measures to protect AI systems from cyber threats and unauthorized access.
Get In Touch
If you have a matter that you would like to discuss then please do not hesitate to contact our team on 0161 000 000 or alternatively you can fill out our online enquiry form below.