With careful planning and execution, AI will transform how, when, and by whom work gets done. All the talk is currently about generative AI, but many other branches of AI, from robotics to machine learning, continue to transform business. Calibrating the security, privacy, and ethical implications inherent in these technologies is challenging, and organizations are looking to establish frameworks that provide both risk management and governance when implementing AI.

AI’s current path: Limited guardrails, but opportunities abound

The concern over business outcomes and the need to foster trust among employees and customers, specifically, and society, in general, has sparked a broad ethical debate around how AI can be controlled and deployed responsibly, transparently, and with integrity. To that end, regulation in this space is ramping up. The public and private sectors must work together to offer practical solutions for support during innovation and development to ensure security and privacy are embedded from the outset.

There is some trepidation in the market to innovate because of the cautionary headlines, the lack of regulatory guardrails, and the absence of a universal standardized global approach to AI. But that unease is being met with an equal measure of passion for AI’s potential to spur innovation.

While we encourage organizations to move forward with the exciting and vital work they’re doing with AI, at the same time, they should ensure they have a thorough understanding of the complexities involved and how to de-risk their models effectively. As the market develops, it's important to allow global regulators and legislators the time to establish meaningful guidelines for AI development. The EU AI Act is a leading example. This landmark legislation is poised to do for AI what the EU’s General Data Protection Regulation (GDPR) has done for privacy, paving the way for exciting and responsible advancements in this field.

Although the absence of legislation is a clear speed bump, the good news is existing privacy legislation has similar principles that can and should be applied to new AI algorithms. Privacy factors such as notice, consent, explainability, transparency, and risk of harm are all codified in existing law.

To remain competitive in the market, CISOs should partner with Chief Data Officers and Data Protection Officers to support the business objectives that are reliant on AI and determine how to harness this game-changing technology effectively and responsibly. At the same time, they need to wrap sufficient governance and controls around processes that may have operated largely without oversight for some time. This harmony between enablement and governance is where successful adoption lies.

Primary challenges in balancing AI innovation with security and privacy concerns

To facilitate their adoption of AI, organizations must make crucial choices that will shape their approach, such as determining whether to create in-house models or rely on third parties. While it may seem that one option is less uncertain, the truth is that both come with inherent risks that organizations must recognize and effectively manage.

Organizations must educate themselves about the safeguards around transparency, accountability, fairness, privacy, and security so they can innovate and deploy with confidence. For example, look to large technology companies and jurisdictions that are further along in their AI journey for guidance around responsible development.

From a privacy and security perspective, many organizations are having their hands forced in a sense. With so many business units moving full steam ahead with AI, CISOs and Chief Product Officers (CPOs) must follow along and ensure the necessary controls are installed. Establishing and maintaining trust in those AI solutions from the outset is critical, for the brand and the ability to meet its business objectives.

This requires cross-functional cooperation, especially from a funding perspective. But to thoroughly embrace and pursue the innovation opportunities, organizations need to agree on a unified security, privacy, data science and legal strategy.