We're at a pivotal moment where AI is no longer theoretical. It's in everyone's hands. That accessibility is fuelling innovation, but it's also creating blind spots.
Understanding AI’s risks and challenges
Generative AI amplifies many of the cybersecurity risks organisations have faced for years, including social engineering, data leakage, and governance gaps. Nearly half of surveyed leaders report that data security concerns are holding back further investment in AI [1]. The risks are tangible. For example, The Netherlands’ childcare benefits scandal, driven by an early machine learning algorithm, led to thousands of innocent families being wrongly flagged as fraudulent. The consequences included financial hardship, public outrage, and even loss of life. This case highlights the reputational, legal, and ethical risks that can arise when AI systems lack proper oversight.
Broader challenges include expanding attack surfaces, ethical concerns such as bias and over-automation, and insufficient governance. As AI technologies evolve, often outpacing regulation, legislation like the EU AI Act is becoming increasingly important.
Building a cohesive AI governance strategy
While risks around data, privacy, and model bias are real, we must also consider the broader impact on workforce dynamics, adoption, and potential overreliance on AI. Thoughtful planning that integrates technology with human capital and change management can significantly accelerate ROI on AI investments.
To keep pace with technological change and evolving legislation, organisations should establish AI governance committees with cross-functional representation. A cohesive governance strategy includes:
AI should be integrated into existing risk, compliance, and governance frameworks, with safeguards applied throughout its lifecycle.
Practical steps to take today
Risk often stems from a lack of understanding. By fostering a culture of awareness, organisations can turn unknown risks into manageable challenges. Nearly half of organisations are already providing employee training on secure and ethical AI use, and 46% are deploying AI-specific security tools [2].
To address today’s risks, organisations should:
AI is expected to have the greatest impact in cybersecurity (55%), compliance monitoring (52%), and supply chain management (50%) [3]. The time to act is now.
Future proofing AI Adoption
Organisations that embrace a ‘fail fast, learn faster’ mindset are better positioned for successful AI integration. AI adoption is not just about innovation—it’s about building resilience and agility in a dynamic business environment. As technology evolves, it’s essential to set clear goals, continuously monitor AI performance, update key risk indicators, and champion regulatory changes. This approach enables organisations to build resilient governance that evolves with AI, rather than resisting change and risking exposure to emerging threats.
We recommend having an AI strategy, someone who is accountable for AI, and an AI governance committee consisting of multiple stakeholders. This ensures it’s not a single point of failure.
Eoghan Daly