Building Digital Resilience: Tackling Cyber and AI Risks

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In today’s digital age, organisations are heavily reliant on technology, making digital resilience paramount. This involves fortifying defences against two primary risks: cyber threats and artificial intelligence (AI) risks.

Cyber Risks: The CIA Triad
Most cyber risks fall under the well-known CIA (Confidentiality, Integrity, and Availability) triad. Confidentiality breaches can lead to data leaks, integrity issues involve tampering or modification of information, while availability risks include system downtime or destruction. These can result in revenue loss, costly claims, fines, and reputational damage.

AI Risks: Beyond Cyber
While the CIA triad applies to AI risks, the attack methods differ. Techniques like prompt injections can target data theft and system functionality. Confidentiality risks include data privacy violations and intellectual property loss. Availability risks encompass system unavailability, ransomware attacks, and functional corruption where the system is rendered ineffective. Integrity risks involve data inaccuracies, biases, and model poisoning, leading to flawed decision-making.

Ethical Considerations AI also introduces ethical concerns around bias, fairness, transparency, accountability, and autonomous dependency. Organisations must address these to avoid legal consequences, regulatory fines, brand damage, and customer loss.

Building Cyber Resilience to Cyber Risks
This requires a multi-layered approach involving preventive security measures like encryption, access controls, and digital signatures, as well as recovery strategies like backups and incident response plans.

Building AI Resilience

Many cyber resilience measures apply to AI, but additional steps are needed. These include:
• Protecting sensitive data accessed by AI systems
• Conducting adversarial testing and employing techniques like fairness-aware machine learning
• Upskilling employees and conducting due diligence on AI suppliers
• Ensuring human oversight and inclusive design principles
• Maintaining transparency and adhering to regulatory frameworks
• Collaborating across business units for AI governance

Resilience is an ongoing commitment to data quality, model validation, transparency, security, and ethical considerations. By implementing these strategies, organisations can create robust, trustworthy, and adaptable AI technologies that navigate the complexities of our digital landscape.

This has been adapted from an article by Zurich which can be found here.

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