Protecting Data Used in Artificial Intelligence

 

The use of artificial intelligence (AI) is rapidly growing in many industries and across society. AI systems like machine learning algorithms rely heavily on data to function. As the use of AI expands, protecting the data used to train these systems is becoming increasingly important. Here are some key considerations around securing data for AI:

• Anonymize data where possible - Removing personally identifiable information from datasets can help protect privacy. Data should be anonymized unless it is absolutely necessary to have identifying attributes.
• Limit data access - Only allow essential personnel to access datasets used for AI model development and training. Put controls in place to monitor who accesses the data and what they do with it.
• Encrypt data - Encrypt datasets, especially when storing or transmitting them. This protects the data at rest and in transit. Use strong encryption standards.
• Carefully select trustworthy vendors - When partnering with outside organizations on AI projects, perform due diligence to ensure they have strong data security practices and that proper contractual protections are in place.
• Track data lineage - Document where datasets originate and how they move through the AI model building pipeline. This supports auditing and helps identify vulnerabilities.
• Develop a data deletion plan - Have procedures in place to delete datasets when they are no longer needed for the AI system. This reduces the risk of old data exposure if a breach does occur.
• Continuously monitor for suspicious activity - Employ tools to monitor datasets and access patterns in order to detect potential security incidents. Act quickly on any abnormal activity.
• Comply with regulations - Stay up to date on evolving data protection laws and regulations applicable to the jurisdictions where the AI system is used. Build compliance into development processes.

Protecting the data that fuels AI is critical as these technologies become more advanced and integrated into our lives. Organizations must make data security a priority when developing and deploying AI systems. With careful controls and governance, data can be secured throughout the AI model lifecycle.

Some examples of data security practices to look for when evaluating potential AI partners:

• Encryption - The partner should encrypt data in transit and at rest using industry standard encryption methods like TLS, SSL, and AES-256. This protects against data theft or inadvertent exposure.
• Access controls - Strict access controls should be in place to limit access to data to only authorized personnel. Look for role-based access, multi-factor authentication, and automated access reviews.
• Anonymization - Data should be anonymized through removal of personal identifiers when possible. Partners should have a data anonymization plan.
• Staff training - Partner staff should receive regular data security and privacy training to instill good security habits when handling data. Ask about their training program.
• Third-party risk management - Partners should vet any third-party vendors that may access data. Look for comprehensive vendor risk assessment processes.
• Data minimization - Partners should only collect, process, and store the minimum data required for the AI system to function optimally. Excessive data creates unnecessary risk.
• Hardware controls - Data should be stored on encrypted drives and servers kept in secured facilities with restricted physical access.
• Data deletion processes - The partner should have data retention policies and procedures to permanently delete data no longer required for the AI system.
• Compliance certification - The partner should comply with relevant data protection laws and be able to provide proof of certifications like ISO 27001 or SOC 2 Type II audits.
• Incident response plan - Partners should have an incident response plan that is regularly tested to handle potential data breaches effectively.