AI Governance Library

Artificial Intelligence and Cybersecurity: Balancing Risks and Rewards

AI systems bring new opportunities—but also novel vulnerabilities. This white paper offers a structured framework for balancing innovation with cybersecurity, aiming to embed cyber risk management throughout the AI lifecycle.
Artificial Intelligence and Cybersecurity: Balancing Risks and Rewards

⚡ Quick Summary

This World Economic Forum white paper—produced in collaboration with the Global Cyber Security Capacity Centre at Oxford—addresses the urgent need to manage cybersecurity risks associated with AI adoption. As organizations scale their AI initiatives, they face not just new attack surfaces but also evolving threat actors who now use AI to enhance their own malicious capabilities. The report presents a comprehensive, seven-step approach for business leaders and senior risk owners to assess, mitigate, and monitor these risks across the AI lifecycle. From recognizing vulnerabilities like data poisoning and prompt injection to advocating for “shift left, expand right, and repeat” practices, the document equips executives with tools and strategic questions to align AI innovation with enterprise-level risk management.

🧩 What’s Covered

The paper is structured around three key cybersecurity dimensions of AI:

  1. AI as a Cybersecurity Threat Target – Emphasis is placed on the increased attack surface that AI introduces. New vulnerabilities (e.g., model evasion, training data poisoning, inference manipulation) require both adaptation of traditional cyber hygiene and new AI-specific controls.
  2. Steps for Risk Management – A structured 7-step framework is proposed:
    • Understand context: Position in AI supply chain, AI autonomy, business and geographical factors.
    • Clarify rewards: E.g., productivity gains, new services, operational efficiencies.
    • Identify vulnerabilities: Across inputs, model architecture, outputs, and interfaces (illustrated in Figure 3, p.16).
    • Assess business impact: New harms include explainability gaps, privacy breaches, and compromised model reliability (Figure 4, p.17).
    • Mitigate risks: Through both existing controls (e.g., asset inventories, access control) and new ones (e.g., prompt curation, adversarial red-teaming).
    • Balance residual risk: Weighing benefits against potential compromises.
    • Repeat: Constant reevaluation across the AI lifecycle.
  3. Roles and Actions for Leadership – Leaders are urged to embed cybersecurity into AI strategy from the outset. Key questions help assess governance readiness, including the organization’s risk tolerance, stakeholder involvement, and deployment tracking mechanisms .

Supporting these themes are real-world risk examples, such as LLM-powered phishing achieving >95% cost reduction with improved success rates , and AI tools autonomously discovering and exploiting zero-day vulnerabilities.

💡 Why it matters?

As AI moves from pilot projects to mission-critical deployments, security cannot be an afterthought. This report bridges a gap in AI governance by giving leaders clear, actionable guidance on how to treat AI systems as cyber assets with unique threat profiles. By reframing AI cybersecurity not just as a technical concern but a strategic leadership issue, the report empowers executives to confidently innovate while avoiding reputational damage, regulatory breaches, or operational disruptions. The “shift left, expand right, and repeat” mantra offers a lifecycle-based mindset needed for AI resilience.

❓ What’s Missing

While the framework is clear, the document offers limited operational detail on how to implement specific mitigations (e.g., what constitutes an effective AI red-teaming toolkit or real-world examples of model rollback procedures). It also doesn’t address SMEs or less-resourced actors, who may lack the capacity to implement complex governance structures. Moreover, sector-specific adaptations—especially for healthcare, finance, or government—are deferred to future publications.

👥 Best For

  • Chief Information Security Officers (CISOs)
  • AI governance professionals
  • Risk officers and compliance leads
  • Senior business executives deploying AI
  • Policy-makers building national cybersecurity frameworks
  • Researchers in AI risk and security

📄 Source Details

TitleArtificial Intelligence and Cybersecurity: Balancing Risks and Rewards

Publisher: World Economic Forum

In collaboration with: Global Cyber Security Capacity Centre, University of Oxford

Publication Date: January 2025

Lead Authors: Louise Axon, Joanna Bouckaert, Sadie Creese, Akshay Joshi, Jamie Saunders

Download linkweforum.org

📝 Thanks to

The AI Governance Alliance, Prof. Sadie Creese and the Global Cyber Security Capacity Centre (University of Oxford), and dozens of cybersecurity experts from industry, academia, and government listed in the contributors section.

About the author
Jakub Szarmach

AI Governance Library

Curated Library of AI Governance Resources

AI Governance Library

Great! You’ve successfully signed up.

Welcome back! You've successfully signed in.

You've successfully subscribed to AI Governance Library.

Success! Check your email for magic link to sign-in.

Success! Your billing info has been updated.

Your billing was not updated.