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Building an AI system is more than creating algorithms, code and data. It also comes with responsibilities to ensure that no part of the system is created from stolen data, copied without permission, or could potentially cause significant harm if the system is hacked or used inappropriately.
In this 4-part series we’d like to share some important considerations at every stage of AI development.
AI systems can result in new proprietary datasets such as internal analytics or user behavior data generated through an AI-powered interface. This can also include data that the AI builds or refines over time, especially when combined with licensed or acquired datasets.
These new datasets can provide a competitive edge and should be kept secret as unauthorized use, leaks, or exposure of this data can harm a company’s market advantage. Therefore, ensure access to the data is strictly limited through access controls, non-disclosure agreements (NDAs), and encryption.
Data Privacy Laws
In Canada, The Personal Information Protection and Electronic Documents Act (PIPEDA) sets out the ground rules for how private-sector organizations collect, use, and disclose personal information. Similar regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) set strict rules for companies handling personal information.
Canada's proposed Consumer Privacy Protection Act (CPPA) and Artificial Intelligence and Data Act (AIDA) aim to strengthen privacy standards and compliance, requiring businesses to enhance data governance. AI firms must anonymize, pseudonymize, or secure data to stay compliant and avoid large fines.
Cyber security
When building AI systems, ensuring robust cybersecurity is critical to protect sensitive data, including intellectual property, personal information, and business operations. Adhering to cybersecurity frameworks and certifications that show your AI system handles user data safely and responsibly like SOC 2 (an internal report on System and Organization Controls) or SOC 3 (an external report) help demonstrate a commitment to securing data and maintaining privacy. Implementing these standards not only helps mitigate risks but also builds trust with clients and partners, ensuring that data used in AI models is properly safeguarded. Learn more about cyber security considerations for consumers of managed services (ITSM.50.030) - Canadian Centre for Cyber Security
Putting Privacy and Security Regulations into Practice
Symend (Calgary) uses behavioral science and AI to help businesses engage customers and recover debts. A key part of their model is following privacy laws and incorporating strong governance policies.
Their Privacy Policy outlines the personal data they collect, how it is used and kept Secure.
As AI development advances, managing data creation and storage responsibly is essential. Protecting proprietary data, following privacy laws, and maintaining strong cybersecurity are critical steps to reduce risks and ensure compliance. Taking these measures helps safeguard your AI system and builds trust with users and partners. In the next part, we will look at the functionalities and features of AI systems and what to consider to keep your technology secure and effective. Stay tuned!
About the Author:
Allessia Chiappetta is a second-year JD candidate at Osgoode Hall Law School with a keen interest in intellectual property and technology law. She holds a Master of Socio-Legal Studies from York University, specializing in AI regulation.
Allessia works with Communitech’s ElevateIP initiative, advising inventors on the innovation and commercialization aspects of IP.
Allessia regularly writes on IP developments for the Ontario Bar Association and other platforms. Allessia is trilingual, speaking English, French, and Italian.
