AI Demands a New Risk Architecture: Why Privacy Must Orchestrate the Convergence

Cassandra Maldini, AIGP, CIPP/US, CIPM, FIP, Vice President AI Governance & Privacy, Securiti
Helen Mouser, CIPP/E, CIPM, CIPT, Senior Privacy Counsel, Dunnhumby

AI is accelerating faster than our governance models can keep up—and it is exposing the fault lines in how organizations manage risk. Right now, privacy, security, and risk teams are often solving the same data with different playbooks. GenAI systems like Microsoft Copilot, AWS Q, and internal LLM deployments do not create neatly categorized issues; they trigger privacy, security, legal, and ethical risks simultaneously. Treating those risks as separate domains slows oversight, scatters accountability, and leaves gaps in control. This is where privacy leaders can — and must — step forward. They understand data at its most granular, contextual, and consequential levels, uniquely positioning themselves to architect a new model of converged data risk governance that unifies oversight across domains without diluting their expertise. As AI transforms the enterprise, risk itself must converge. Privacy professionals are best placed to design that future.

What you will learn:

• Reframing privacy from a compliance function to a core node in enterprise risk architecture.
• Designing shared risk taxonomies and intelligence systems across functions.
• Harmonizing legal and technical controls to govern AI systems at scale.
• Creating unified command structures that enable fast, coordinated risk decisions.

Sponsored by: Securiti