Always ask for and verify the sources or references behind GenAI outputs.
Maintain a critical mindset toward GenAI outputs and validate with secondary sources, especially for cybersecurity-related tasks. Using your eyes and ears is still the best way to differentiate between AI-generated and genuine human content.
Look for user reviews and documented case studies that illustrate real-world performance and reliability.
Request clarity on the data sources and methodologies used to train AI to assess potential biases thoroughly. Most biased outputs can be traced to training data sets that were not carefully curated, and which were unrepresentative of the group for which the output would be used.
Avoid investing in AI for its own sake; instead, focus on solving pain points where clear value can be demonstrated.
Utilize model cards to document how your team assesses and mitigates risks (e.g., bias and explainability) and make this information available to users.
Conduct regular audits of AI performance and fairness to identify and address any issues proactively. Bodies such as the European Data Protection Board and the Dutch-based ICT Institute have published helpful checklists on how to conduct such audits.
Define clear accountability structures and governance policies (such as ethics boards and external audits) around your AI systems to bolster user trust.
Educate your users about how AI works, including its limitations and the measures taken to ensure its reliability, Use clear, jargon-free communication to demystify AI and build trust.
Be careful to balance the rush to grab commercial gains that AI offers with the paramount need for trust. Put in place strong systems that continuously challenge the trustworthiness of the AI you use and with which your customers will interact.
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