Guide: Security Risks in AI – Balancing Innovation and Exposure

AI’s potential for innovation comes with equal parts vulnerability. Complex models, opaque algorithms and large data sets make it difficult to fully understand and secure their AI systems. Smaller businesses are often most exposed to data privacy issues and unauthorized tool use, while larger enterprises face adversarial attacks, compliance pressures and supply chain risks.

This guide highlights key AI security risks and practical steps for managing them. Download the full guide for a deeper look at how these threats affect organizations of different sizes and maturity levels, along with tailored recommendations for addressing them effectively.

Whether an organization is just starting to experiment with AI or scaling enterprise-wide deployments, managing these risks requires clear policies, consistent monitoring and employee awareness. Smaller organizations benefit from vendor-provided security controls and strong access management. Larger enterprises should complement those measures with third-party audits, zero-trust architectures and adversarial resilience testing.

One of today’s fastest-growing risks stems from the use of unapproved AI tools at work. Shadow AI can inadvertently expose proprietary or client data, create compliance gaps and weaken overall governance.