August Issue
code4thought
Enhancing Quality & Security of Software and AI Systems: We make technology trusted and thoughtful
As organizations embrace AI and accelerate software delivery, the stakes for trust, compliance, and reliability have never been higher…especially now that the next phase of the EU AI ACT is in full swing!
There is a reality every auditor — and every organization deploying AI — must face: trustworthy AI is not just a technical challenge, it’s an auditing imperative.
1. "AI Fundamentals for Auditors" workshop with TUV Hellas
This is exactly what was underscored in our recent workshop on AI Fundamentals for Auditors with TÜV Hellas.
Key themes included:
Our view ➡️ Auditors who embrace AI governance today are not just ensuring compliance — they’re positioning themselves as enablers of responsible innovation.
As AI embeds itself into the critical infrastructure of business, evaluation, auditing, and software quality are no longer “support functions.” They are maturity markers! The question is no longer “Is it accurate?” but “Is it meaningful?”
2. Four principles for meaningful AI evaluation
Too often, evaluations are treated as an afterthought — a few metrics checked at the end of development. But in reality, evaluation is strategy: it shapes business clarity, product reliability, and stakeholder trust.
Our blueprint proposes four principles for meaningful AI evaluation:
1️⃣ Purpose-Driven — align KPIs with real-world outcomes, not just technical scores.
2️⃣ Testing Readiness — ensure data, access, and experts are in place from the start.
3️⃣ Contextual & Rigorous Execution — evaluate not only for correctness, but for consequence.
4️⃣ Actionable Communication — transform evaluation insights into business decisions.
3. The AI Speed Trap: When Speed Outpaces Stability (Based on Andrew Power, TechRadarPro, Aug 2025)
The rise of Generative AI has supercharged productivity. Code is being shipped faster, testing automated, and releases accelerated. But speed is masking a deeper issue: software quality is falling dangerously behind.
Two-thirds of global organizations face serious outage risks within the next year.
Almost half estimate losses of $1M+ annually due to poor software quality.
The quality gap widens when AI is trusted to generate and deploy code without rigorous oversight.
The tension is clear: speed vs. stability.
AI can accelerate delivery, but without strong QA, governance, and audits, it also accelerates risk: outages, breaches, mounting technical debt.
Our perspective: The organizations that win in the AI era will be those that match velocity with vigilance. Quality, trust, and resilience must scale at the same pace as automation. Otherwise, the AI speed trap will turn today’s productivity gains into tomorrow’s liabilities.