Secure by Default: Why AI-Driven Delivery Needs a Rethink
By Dewan Ahmed
Software delivery has been accelerating for more than a decade, and the arrival of AI has pushed us into an entirely new velocity class. Code generation, configuration scaffolding, infrastructure suggestions, remediation hints, and deployment decisions now involve AI. It participates in every stage of the delivery pipeline.
On the surface, this feels like progress. Faster delivery, fewer bottlenecks, happier teams.
But a closer look reveals something more complicated.
The Gap Between Speed and Safety
Most modern pipelines were designed for a world where humans made the important decisions, such as writing code, reviewing changes, crafting configurations, and evaluating risks. AI changed that dynamic. It introduced automation that moves faster than traditional security practices can respond to.
The result is a widening gap between the speed at which we can ship and the safety at which we can ship.
Here are a few of the challenges teams are already feeling today.
1. AI is amplifying existing weaknesses
If your base images are outdated, your tests barely cover your codebase, or your deployment strategy lacks guardrails, AI does not fix these issues. It accelerates them.
Pipelines automate mistakes with the same enthusiasm they automate features.
2. The threat surface has shifted
The most common AI failures today are not the classic vulnerabilities we are used to scanning for.
Instead, organizations are seeing issues such as:
- prompt injection
- model jailbreaking
- vulnerable AI-generated code
- behavior-driven exploits
- configuration drift caused by AI tools
These attacks target behavior and reasoning, not code structure.
3. Delivery is faster, but security has not kept up
AI-assisted workflows generate code, configurations, and infrastructure definitions at machine speed. Most security models still rely on manual reviews, point-in-time scans, or human approval gates.
This mismatch means misconfigurations are no longer slow, human-scale mistakes. They have become automated hazards.
4. Developers are not set up for AI-era security
Most developers have little or no training in AI security or AI threat modeling, yet we expect them to recognize model manipulation, unsafe outputs, and subtle behavioral failures in AI-generated artifacts. The expectations do not match the support we give them.
These challenges will define the next era of DevSecOps. If you want to hear about a practical four-pillar framework for secure-by-default AI-driven delivery, join my keynote at Conf42 DevSecOps 2025.