Building an Effective Strategy to Manage AI Risks

Infrastructure hardening: Strengthen your organization’s AI/ML infrastructure so that it can handle extensive training data — securely. You can achieve this by building strong access controls over who can access training data, setting multifactor authentication protocols for any access to the infrastructure, patching the infrastructure, and so on. Test the strength of your AI/ML infrastructure with red teaming or penetration testing. 

Alerting and monitoring: Develop robust monitoring capabilities to detect and prevent theft of your proprietary AI models.

Data leakage prevention: Adequate governance over the data being input into third-party AI systems is crucial; after all, this is what these AI systems will be trained on. Implement data leakage prevention solutions and strategies to monitor the sensitive data flowing into your and third-party AI systems, and ensure all AI solutions are vetted before uploading any sensitive information.

Employee training: Educate employees on AI-related risks, such as deepfakes and voice-based vishing attacks. Regularly test employees on their abilities to identify and thwart potential attacks.

Evaluate, evaluate, evaluate: Continuously evaluate the ethical implications of using AI technologies within your organization, and cross-check your AI and data practices with new regulation to ensure you remain compliant.

And educate, educate, educate: With the rapidly changing landscape, it’s crucial to continuously educate and enable your employees on risks and organizational policies for AI usage.

AI risk assessments: Own the process of conducting risk assessments for AI projects to identify and mitigate privacy and compliance risks. 

Privacy by design: Implement privacy-by-design principles in AI development to build privacy-focused AI systems. These include ensuring consumer consent gathering strategies are understood, putting privacy safeguards in place to protect personal data, and implementing data anonymization strategies when training models.

Maintaining transparency and user preferences: Build workflows to ensure transparency and manage user consent, preferences, and data rights effectively.

AI task force: Develop a cross-functional AI task force within the organization with representation from key stakeholders across privacy, security, and governance. This task force will be responsible for developing AI guidelines and policies for the organization, approving the procurement of new AI technologies to carefully evaluate and mitigate AI risks, and ensuring that AI safety is prioritized when new systems are developed. Schedule regular meetings (monthly, for example) for the AI task force to discuss industry trends, emerging threats, and new use cases for AI technologies.

Dedicated communication channels: Establish channels where employees can easily ask questions and receive timely information about approved AI tools and their use. Utilize cross-functional forums like company all-hands or department-specific all-hands meetings to communicate organization-wide updates pertaining to AI use and considerations.

Leveraging technology and automation: The rise of AI systems has led to innovative technologies designed to identify and catalog AI systems within your organization and supply chain. It is essential to evaluate and implement these tools to enhance your AI governance framework. Integrating these advanced technologies, as well as solutions like SecurityPal for navigating security reviews, can help you maintain oversight, ensure compliance, and effectively manage the growing complexity of AI applications.