About
AI is no longer a pilot experiment inside engineering. It is becoming embedded in how code is written, reviewed, and secured. As Copilot evolves toward agentic capabilities and security shifts left through advanced scanning and automated remediation, leadership questions change.
How much autonomy should AI have inside the SDLC?
How do we quantify productivity without expanding risk?
What governance models are required when code is partially machine-generated?
This executive roundtable explores how CXOs and engineering leaders can build trust in AI-augmented development. We will examine real workflows where AI assists in remediation, enforces policy, and reduces mean time to fix, and discuss the operating model changes required to scale this responsibly.
The goal is not faster coding. It is resilient, measurable, and board-ready engineering confidence in the age of AI.
Key Discussion Points
- AI embedded across coding, review, testing, and remediation
- Assistive vs. advisory vs. autonomous AI models
- AI’s role in reducing Mean Time to Detect (MTTD) & Mean Time to Fix (MTTF)
- Automated policy enforcement in CI/CD pipelines
Why Attend?
- Learn how to align AI-augmented engineering with risk, compliance, and measurable business outcomes.
- Explore practical governance models for controlling AI within the SDLC.
- Benchmark how peer CXOs are managing AI autonomy and productivity.
- Walk away with actionable strategies for secure, scalable AI-driven engineering
Speakers
Nahas Mohammed
Regional Sales Director
GitHub
Vicky Israni
Sr. Solutions Engineer
GitHub