To address these challenges and risks, organizations need to develop and implement best practices and standards tailored to their specific business needs, striking the right balance between enabling innovation and introducing risk.
While guidelines like NCSC Secure AI System Development and The Open Standard for Responsible AI provide a valuable starting point, organizations must also develop their own customized best practices that align with their unique business requirements, risk appetite, and AI/ML use cases. For instance, a financial institution developing AI models for fraud detection might prioritize best practices around data governance and model explainability to ensure compliance with regulations and maintain transparency in decision-making processes.
Key considerations when developing these best practices include:
- Ensuring secure data handling and governance throughout the AI life cycle Implementing robust access controls and identity management for AI/ML resources
- Validating and monitoring AI models for potential biases, vulnerabilities, or anomalies
- Establishing incident response and remediation processes for AI-specific threats
- Maintaining transparency and explainability to understand and audit AI model behavior