The 2026 Threatscape

AI-powered digital transformation creates a critical security paradox: the systems accelerating innovation are prime vulnerability points. As our data volumes multiply with AI pipelines, organizations face new risks that traditional security frameworks never needed to cover. With AI-related breaches projected to increase significantly in 2025, securing these data flows has become a board-level priority.

Security teams have to be able to balance transformation speed with robust protection across the entire AI ecosystem. This requires rethinking data governance, implementing continuous validation protocols and establishing clear visibility into automated decision paths—all while maintaining the business velocity that AI enables. The challenge isn't just technical; it requires new governance models that align security, data science and business objectives.

Join Oksana Denesiuk (Senior Product Manager, Solution Consultant - Billing, Kaiser Permanente) to discover practical frameworks that will secure AI systems without sacrificing innovation speed.

Key takeaways:

- Identify hidden vulnerability points in AI pipelines that traditional security tools miss
- Implement data lineage tracking and validation protocols across your automation ecosystem
- Establish board-ready metrics that measure AI security posture and data protection efficacy
- Deploy zero-trust principles specifically designed for machine learning environments
- Balance security controls with the speed and scale benefits that drive AI adoption