Market observers in information security are pressing for the creation of an AI safety center anchored by a regulator to address the security issues that come with artificial intelligence. The vision is a center that can oversee governance, publish practical standards, and coordinate efforts among regulators, industry players and researchers in Canada and the United States as AI use expands. Such a hub would help harmonize approaches to threat intelligence, incident response and policy alignment, enabling more consistent risk management across borders and sectors.
Experts warn that there is still no unified database for AI threats or reliable user identification mechanisms. Many teams rely on open source libraries to extract images and text from data, yet some of these tools have been compromised. Public information on how to manage these risks remains incomplete and uneven. The lack of a centralized repository for AI risk signals and identity verification hinders timely detection and coordinated defense, leaving organizations to improvise rather than execute structured protection plans.
Because such a mechanism does not yet exist, proponents argue for a regulator backed center that focuses on AI security, including controls in software used to develop AI and the evolving ways attackers can manipulate AI models. The goal is to establish clear guardrails for how AI systems are built, tested and deployed. This includes addressing model provenance, secure coding practices, and rigorous evaluation of training data so that vulnerabilities do not propagate into deployed systems.
One proposal is to collect data on AI cyber risks in CERT style centers that emphasize rapid response and information sharing. This approach has been discussed for several years and is gaining renewed attention as AI becomes more embedded in critical services. The proposed centers would aggregate anonymized incident data, share best practices, and monitor emerging threats, creating a feedback loop that strengthens defensive capabilities across industries and government agencies.
Many experts believe the most effective path is a single center that evaluates AI safety on a regulatory platform. In classical cybersecurity, information exchange about attack methods, tools and signs of compromise is already common, but protecting AI requires new methods that join policy, technical controls and continuous risk assessment. A centralized framework would facilitate standardized testing, model risk management and transparent disclosure of safety incidents, while supporting cross-sector collaboration and international cooperation to keep pace with rapid AI advancements.
Recent surveys indicate that a significant portion of metropolitan enterprises already deploy AI in their operations. This reality underscores the need for governance frameworks, risk management and standardized security practices to govern AI across industries. As AI becomes integral to decision making, automation and customer engagement, a cohesive approach to safety, privacy and reliability becomes not just prudent but essential for sustainable growth across North America.