The 16th CTO Colloquium, Chicago
The 16th CTO Colloquium: Confronting Quality Debt & The AI Security Gap
“What you do is the only thing that keeps me up at night.”
AI has done something remarkable for engineering organizations: it’s made the development side of software nearly infinite. Code generation has never been faster. Releases have never been more frequent. And the gap between what your team is shipping and what your team can actually verify has never been wider.
That gap has a name. Quality debt. And sitting inside it, often invisible until it’s expensive, is something worse: an AI security surface that most engineering teams haven’t mapped, because the frameworks built to catch traditional vulnerabilities weren’t designed for code that wrote itself.
The security dimension is where this gets serious. AI-assisted development introduces vulnerability classes that traditional security frameworks weren’t built to catch: insecure patterns absorbed from training data, logic errors that look correct until they meet an adversarial input, dependencies pulled in without the scrutiny a human reviewer would apply, compliance drift accumulating silently in codebases moving too fast to audit. Most organizations won’t discover these exposures from a scan. They’ll discover them from an incident.
The 16th CTO Colloquium is where we bring this conversation into the open.
This is a working forum for engineering leaders. We’re convening practitioners who are actively navigating the integrity gap: quality engineers, security specialists, CTOs who’ve already felt what happens when AI-generated code enters production without adequate oversight, and the ones who are determined to get ahead of it before they do.
Core Themes:
The Quality Debt Crisis
AI-generated code doesn’t just bring speed. It brings debt that compounds invisibly. Defect escape rates climb. Regression suites go stale. Test coverage that felt adequate six months ago no longer reflects the codebase. How do engineering organizations identify and quantify quality debt before it becomes a structural liability, and what does it take to build quality systems that can actually keep pace with AI output?
The AI Security Surface
When code generation is the engine and human review is the bottleneck, new vulnerability classes emerge. AI-assisted development introduces subtle, systemic risks: insecure dependencies absorbed from training data, logic errors that pass functional tests but fail under adversarial conditions, and compliance drift no one has audited. We’re bringing in experts in application security and AI-specific code risk to go deep on what engineering teams need to understand, detect, and defend against right now.
The Verification Bottleneck
The hardest thing to preserve in an AI-accelerated org isn’t velocity, it’s ownership. “Founder-level” system knowledge erodes fast when the code is writing itself. We’ll explore strategies for maintaining meaningful human oversight: where to apply it, how to automate the rest, and what it looks like to build a quality culture that scales without losing the signal.
Who This Is For
Engineering leaders, CTOs, VPs of Engineering, Chief Software Engineers who are done pretending the quality and security implications of AI development are someone else’s problem. If you’re the person in your organization responsible for what ships and what doesn’t, this room was built for you.
Agenda
Day 1: Thursday — July 23
Afternoon & Evening Sessions
3:00 – 3:30
Opening remarks & light refreshments
3:30 – 4:10
Speaker 1
4:10 – 4:50
Speaker 2
4:50 – 5:15
Break
5:15 – 5:55
Speaker 3
5:55 – 6:35
Speaker 4
6:35-6:45
Debrief
7:00
Optional networking dinner (Dutch treat)
Post-event drinks and informal networking, venue TBA
Day 2: Friday — May 1
9:00 – 12:00
CTO Forum Session
A facilitated, peer-driven forum designed to surface real challenges, shared patterns, and actionable insights from the group.
12:00 - 1:00
Lunch
We’re also actively seeking speakers with practitioner-level expertise in:
-Application security in AI-assisted development environments
-Static analysis, SAST/DAST tooling, and LLM-specific vulnerability patterns
-Secure code review practices for AI-generated output
-Compliance and regulatory exposure introduced by AI coding tools
-Real-world incident response involving AI-generated code failures
If you’ve lived this problem — or built something that addresses it — we want to hear from you.
Presented by 7CTOs
CTOs face daily high-stakes hurdles. 7CTOs is an exclusive tech leader membership providing expert support, coaching and community to help CTOs lead, grow, and thrive.