Enterprise AI adoption is accelerating at an unprecedented pace — yet security maturity is struggling to keep up.
From model supply chain compromise and prompt injection to data poisoning, membership inference, and multi-agent cascade failures, AI introduces a fundamentally new attack surface that traditional DevSecOps was never designed to defend.
In this thought-leadership webcast, we will introduce a structured, lifecycle-driven MLSecOps framework that embeds security from AI strategy and model selection through deployment and sustained operations. Attendees will discover how to:
- Identify AI-specific risks before development begins
- Tier and activate controls based on architecture, exposure, and business impact
- Secure data pipelines, training artifacts, vector stores, and agent frameworks
- Validate models against adversarial manipulation, privacy leakage, and unsafe outputs
- Operationalize governance through drift detection, continuous validation, and measurable assurance
We will also examine emerging regulatory expectations and how they are reshaping executive accountability for AI systems across industries.
If your organization is building, integrating, or deploying AI at enterprise scale, this session will provide a perspective to move from reactive controls to operationalized AI security — enabling innovation without sacrificing trust, resilience, or compliance.
Who Should Attend
- CIOs
- CTOs
- CROs
- CISOs
- Security architects
- Security and risk & compliance leaders
This webinar supports content and knowledge from LDR520: Emerging Trends for Cyber Leaders: AI and Cloud. To learn more about this course and explore upcoming sessions, Click Here.
