From AI-Ready to AI-Governed

Governing Autonomous Development, AI-Driven Code, and Emerging Supply Chain Risk

AI-accelerated development has moved from experimental to operational.

Code is now being written, tested, and deployed with the assistance - and in some cases the full autonomy - of AI systems. While this unlocks unprecedented speed and innovation, is also introduces a fundamentally new class of security risk that traditional models were never designed to handle. 

From LLM-generated polymorphic malware to poisoned open-source models entering enterprise pipelines, the software supply chain is evolving faster than conventional security controls can adapt.

In this environment, permieter security is no longer sufficient. When AI agents can independently generate and deploy code, governance must move directly into the development lifecycle itself.

Join a select group of peer CISOs and senior security leaders for a closed-door virtual roundtable exploring how organisations are adapting to autonomous development and redefining security, governance and compliance for the AI era. 

What We'll Explore

This discussion is designed to move beyond theory and into the real-world challenges security leaders are facing today.

1. The Rise of the Autonomous Supply Chain

As AI agents begin to contribute directly to software development:

• How do you establish identity and trust when no human is directly responsible for code creation?
• What does secure software delivery look like in an environment driven by autonomous agents?
• How do you maintain oversight without slowing innovation?

2. Curation vs Chaos in AI Model Intake

As organisations adopt third-party models and AI components at scale:

• How do you prevent model poisoning and malicious dependency injection?
• What safeguards are required to protect against prompt injection and data contamination?
• Can "trusted AI catalogs" reduce systemic risk across the enterprise?

3. DevGovOps & Regulatory Readiness

Regulatory frameworks are evolving alongside technology. We'll discuss:

• Preparing for standards such as NIST SP 800-218 and EU AI Act
• Embedding compliance into engineering workflows
• Automating governance through trust-by-design architectures
• Aligning security controls with emerging regulatory expectations

4. Managing "Shadow AI"

AI adoption is happening faster than governance structures can respond. Key discussion points include:

• Identifying and reducing unsanctioned AI usage across teams
• Creating secure, approved pathways for experimentation
• Balancing innovation with risk containment
• Building policies that developers and teams will actually follow

5. Board-Ready Metrics for AI Risk

Security leaders are increasingly expected to communicate AI risk in business terms. We'll explore:

• How to quantify AI-related risk reduction
• Defining meaningful governance KPIs for executives and boards
• Translating technical controls into business outcomes
• Demonstrating governance effectiveness at scale

Defining the Future of Secure AI Development

AI is already transforming how software is built. The next challenges is ensuring it is built securely, governed effectively and trusted at scale.

The organisations that succeed will not be those that slow AI adoption - but those that embed governance directly into the systems that create it.

Join your peers to define what AI-governed development looks like in practice.

Register your invitation below. Places are strictly limited to ensure an executive-level peer discussion.