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Staying informed about AI privacy regulations and best practices is critical for privacy officers, senior IT professionals, and compliance leaders. The fourth installment of the AI Mastery Series—“Navigating AI Privacy: Strategies and Challenges”—delivered in-depth guidance for navigating ethical, legal, and operational challenges in AI. Co-hosted by OpenRep ISACA Vancouver Chapter and the Canadian Digital Governance Council, this event brought together leading experts who shared their insights on how to manage AI privacy effectively.
This session we are bringing you Ray Everett, the world's first chief privacy officer of the digital era!
By Ray Everett, JD FIP CIPM CIPP/US, Chief Compliance & Data Privacy Officer, Topcon Healthcare Inc.
The landscape of privacy and security risk management is experiencing an unprecedented transformation. What was largely theoretical just two years ago has become a mission-critical challenge for organizations worldwide: the implementation, and proper governance, of Artificial Intelligence.
Privacy and security professionals find themselves thrust into the role of AI risk managers, expected to provide the same level of comprehensive oversight for AI that took decades to develop for traditional technology risks.
The evolution of technology risk management tells a story of accelerating change. When the first Chief Information Security Officer was appointed at Citigroup in 1994, it marked the beginning of a methodical development of security risk management. Over the next 20 years, we saw the emergence of global standards like ISO 27001 and the development of sophisticated GRC platforms and risk assessment tools in 2009, and the first security orchestration platforms in the 20-teens.
Privacy risk management followed a similar but compressed path – from the emergence of the first Chief Privacy Officers in 1999, to the development of comprehensive privacy management platforms starting around 2014, to the emergence of ISO 27701 in 2018, a privacy information management standard based on the European General Data Protection Regulation (GDPR).
Now, AI governance is demanding the same maturity in a fraction of the time. Companies like IBM introduced the first natural language AI in 2010 with Watson – we all remember IBM’s Watson competing on Jeopardy. But for most of us, our first up-close and personal experience with AI was the release of ChatGPT in late 2022.
In the barely two and a half years since then, organizations have scrambled to establish AI governance frameworks, appoint AI leadership, and implement comprehensive risk management programs. What took more than a decade in security and also a decade in privacy, is being compressed into months with AI.
This acceleration isn't just about timing – it represents a fundamental transformation in how we understand and manage risk. Traditional technology risks were typically binary or quantifiable: data was either breached or not, a system was either vulnerable or patched. AI introduces a new paradigm of probabilistic, evolving risks that challenge our traditional frameworks.
Consider the nature of AI risk: it exists on a spectrum, evolves over time, and can propagate in unexpected ways. A model that performs well today might develop concerning behaviors tomorrow as it learns from new data. Risks that start small can amplify and cascade through interconnected systems at machine speed. Perhaps most challengingly, AI decisions might be technically correct but unexplainable, creating a new category of "black box" risks that defy traditional audit and control mechanisms.
Today's privacy and security professionals face unprecedented expectations. Organizations aren't waiting for them to become machine learning experts – they need guidance now. It's akin to city planners in the early 1900s who were experts at managing horse-drawn carriages suddenly having to reimagine their entire approach for the automobile age. Their fundamental expertise remained valuable, but they had to rapidly adapt to a completely new paradigm that moved faster, scaled differently, and introduced risks they'd never contemplated before.
The solution lies not in waiting for perfect frameworks or regulations, but in developing adaptable governance approaches that can evolve with the technology. The key is fostering a culture of "responsible AI by design" – embedding risk awareness and management practices into the development process itself.
This means:
Most importantly, it means helping organizations understand that AI governance isn't about saying "no" – it is about enabling responsible innovation. Just as privacy professionals evolved from being perceived as roadblocks to becoming essential partners in product development, AI risk managers must position themselves as facilitators of responsible AI adoption.
The acceleration of AI adoption shows no signs of slowing. Privacy and security professionals must embrace their role as AI risk translators, bridging the gap between technical capabilities and organizational risk management. Success will require a delicate balance of adapting existing expertise while developing new frameworks suited to AI's unique challenges.
The goal isn't to become AI experts, but to become expert AI risk managers. This means staying nimble, fostering risk awareness throughout the organization, and building frameworks that can evolve as rapidly as the technology itself.
Perhaps most critically, success in AI governance requires bringing our stakeholders along on this journey. Risk management cannot be effective if it operates in isolation. We must reach out to those on the front lines of AI development and deployment – understanding their pressures, acknowledging their challenges, and sharing our learnings.
This means creating spaces for open dialogue where development teams feel comfortable raising concerns early. It means investing time in teaching and training, helping teams understand not just what the rules are, but why they matter. When a product manager or developer faces an AI risk decision, they should feel confident that they have the tools to make good choices – and know that risk professionals have their back when they need guidance.
By fostering this collaborative approach, we create a multiplier effect. Every stakeholder we empower becomes another set of eyes watching for potential risks, another voice advocating for responsible practices, another partner in building safer AI systems.
The future of AI governance is being written now, at machine speed. Our challenge – and opportunity – is not just to keep pace with the technology, but to bring our entire organization along with us. By building these bridges of understanding and support, we create a foundation for responsible AI that is both more robust and more sustainable than any single framework or policy could provide.
This summary is by Anthony Green, based on Ritchie's session.
Ritchie Po, a seasoned data privacy and technology lawyer, explored the complexities of the Canadian privacy landscape and its relationship to global regulations. His talk underscored the importance of staying current with various legislation that shapes how organizations collect, use, and disclose data.
Ritchie began by explaining how Canada’s privacy framework is made up of a blend of federal and provincial laws. He discussed PIPEDA, as well as provincial regulations like PIPA in British Columbia and Alberta, and Law 25 in Quebec. Emphasizing how this patchwork can create both obstacles and opportunities, he also highlighted Bill C-27, which at the time was proposed as a significant modernization of Canada’s federal privacy laws, but is unlikely to pass in Parliament.
While Canada has its own complex requirements, many businesses also need to comply with international regulations like the GDPR and the CCPA. According to Ritchie, aligning privacy practices across these overlapping jurisdictions is not only possible but also advantageous. Companies that plan for compliance in a proactive, holistic, organic manner can reduce audit fatigue and position themselves competitively in the global marketplace.
Throughout his presentation, Ritchie argued that privacy should be treated as a strategic advantage in business development, rather than a stand-alone legal obligation. He shared success stories of organizations that embed privacy by design into AI workflows and how proactive compliance helps businesses land new clients. This approach helps companies avoid potential legal pitfalls, foster customer trust, build corporate brand name, and ensure smoother regulatory audits.
Looking ahead, he encouraged organizations to invest in continuous education around privacy impact assessments, breach response protocols, and cross-border data flows. By adopting privacy as a “living framework,” businesses can adapt to new technological and regulatory shifts more easily.
Ritchie’s session made it clear that privacy compliance is a cornerstone for any organization aiming to innovate responsibly in the AI arena. The fundamental legal building block of AI is data privacy law. His insights provided a roadmap for companies eager to navigate the multifaceted world of Canadian and global privacy laws.
This summary is by Anthony Green, based on Max's session
Wrapping up the evening, Maximiliano Paz, an AWS Solutions Architect with over 15 years of experience, presented on how to implement responsible AI in real-world contexts. His talk resonated strongly in light of the widespread adoption of generative AI, which raises new ethical and legal questions for businesses.
Max began by defining responsible AI using the eight pillars championed by AWS: fairness, transparency, veracity, robustness, safety, privacy, governance, and explainability. He explained that organizations adopting these principles from the start can reduce the risks associated with AI deployment—ranging from biased outcomes to reputational damage—and can also build trust with customers and regulators alike.
He then highlighted the rapid growth of generative AI in the workplace, citing statistics that show nearly half of professionals now use such tools, often without formal oversight. This trend introduces the possibility of data leaks, accidental sharing of intellectual property, and potential regulatory violations. To address these concerns, Max urged organizations to establish clear policies and technological safeguards that ensure AI is both useful and compliant.
Throughout his talk, Max showcased AWS tools designed to align AI innovation with responsible practices. Amazon Bedrock, for instance, includes watermarking to identify AI-generated content and guardrails that can filter harmful outputs or protect personally identifiable information (PII). In tandem, SageMaker supports every stage of the AI lifecycle—from secure data handling to real-time model monitoring. These combined features enable a more transparent, manageable AI environment that meets regulatory requirements.
Financially, Max noted, the stakes are high: companies that effectively implement responsible AI can see greater profit margins than those lagging in compliance. This profitability, coupled with the ethical and legal imperatives, forms a compelling case for businesses of every size to prioritize responsible AI. He concluded by emphasizing practical steps, such as conducting thorough risk assessments before launching AI solutions, regularly testing and validating model performance, and promoting collaboration among IT, legal, and compliance teams.
Max’s presentation offered a clear framework for responsible AI adoption, complete with actionable tips and AWS-specific resources that can streamline implementation. His approach confirmed that leveraging AI responsibly is not just about meeting regulatory requirements, but also about achieving long-term success and maintaining public trust.
This session of the AI Mastery Series demonstrated that ongoing collaboration and knowledge sharing are vital for navigating AI governance effectively. For privacy officers, IT leaders, and compliance professionals, the evening offered both strategic insights and practical methods to stay ahead in an evolving regulatory landscape.
We’ll update this post with details from the first speaker’s session as soon as they’re available. In the meantime, keep an eye out for upcoming events in the AI Mastery Series, where we’ll continue to explore the intersection of AI, privacy, and responsible innovation.
