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Why AI Systems May Never Be Secure and What Businesses Should Do

Artificial Intelligence (AI) is rapidly becoming embedded in everyday business processes and decision-making. However, a recent Economist article (Sept 22, 2025) highlights a sobering reality: AI systems may never be fully secure. This is not cause for alarm, but rather a call for vigilance and layered safeguards. Here, we summarize the key points, takeaways, and insights relevant for Canadian businesses and financial leaders.

Key Points

  1. Intrinsic Vulnerabilities

    • AI models, especially large language and generative models, are statistical learners that generalize. This makes them inherently susceptible to manipulation.

    • Traditional “patch and defend” security methods are insufficient.

  2. The “Lethal Trifecta” of Risk

    • Complexity and Opacity: Internal workings are difficult to interpret.

    • Generalization: Models can react unpredictably to novel inputs.

    • Real-world Interaction: AI connects with APIs, data pipelines, and external systems, expanding the attack surface.

  3. Limits of Training-Based Safety

    • Techniques like adversarial training and reinforcement learning with human feedback help, but cannot eliminate risks.

    • Cleverly designed prompts can still bypass safeguards.

  4. Attack Surface Beyond the Model

    • Vulnerabilities often lie in the environment: APIs, infrastructure, and data pipelines.

    • The security of these systems is just as important as the AI itself.

  5. Recommended Mitigation Strategies

    • Defence in Depth: Use layered protections like input sanitization, anomaly detection, and kill switches.

    • Red Teaming: Continuously test systems against adversarial attacks.

    • External Oversight: Encourage audits, transparency, and regulatory compliance.

    • Access Controls: Limit exposure by compartmentalizing sensitive tasks.

    • Monitoring: Track outputs and behaviours for anomalies.

    • Plan for Residual Risk: Accept that vulnerabilities will remain; build fallback and human oversight mechanisms.

Business Takeaways

  • Ongoing Process: AI security is not a one-time fix. It requires continuous adaptation.

  • Holistic Approach: Combine technical, organizational, and regulatory strategies.

  • Residual Risk Management: Treat AI like financial risk, mitigate, monitor, and plan for failure scenarios.

  • Deployment Matters: The context in which AI operates can create risks greater than the model itself.

  • Strategic Humility: No system is invulnerable; overconfidence is dangerous.

Insights for Canadian Finance Leaders

  • Risk Governance: CFOs should incorporate AI security into enterprise risk frameworks.

  • Regulatory Preparedness: Canadian businesses should anticipate AI-related compliance and disclosure requirements.

  • Operational Safeguards: Deploy monitoring and fallback systems to protect financial processes.

  • Audit and Oversight: Push for third-party validation of AI security and compliance.

Bottom Line

AI security is best seen as a continuous contest between defenders and attackers. As protections strengthen, adversaries evolve. Complete safety is unlikely, but businesses can approach AI risks much like financial risks: through layered safeguards, active oversight, and well-prepared contingency plans.

 

Next Step for Savvy CFO Clients: Assess where AI touches your financial operations. Identify potential vulnerabilities and then implement layered safeguards to mitigate the risk.

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