AI in software development: Threat or opportunity?

This raises a critical question: what does this shift mean for cybersecurity?

A new generation of risks 

Recent developments highlight how quickly the landscape is changing. 

The rise of autonomous, or agentic, AI systems, meaning systems that can independently plan and execute tasks, introduces entirely new security implications. Early examples such as OpenClaw demonstrate how AI can operate beyond simple prompting into continuous workflows. 

At the same time, advances like Claude Mythos illustrate how powerful these systems can be in identifying vulnerabilities. Mozilla reported how an early version helped identify 271 vulnerabilities in Firefox 150 (see: Mozilla blog). 

This dual capability, both creating and detecting vulnerabilities, makes AI fundamentally different from previous tooling. 

Why traditional security still matters 

Despite the novelty of these technologies, the most effective defenses are still built on well-established principles. 

• Defense in depth 
  Multiple layers of security controls, so that failure in one layer does not expose the entire system 

• Zero trust 
  No user or system is inherently trusted. Everything must be continuously verified 

 These principles remain highly relevant and, in many cases, even more critical in an AI-driven world. 

Key security risks in AI-driven development 

AI introduces several distinct categories of risk. The most relevant ones in practice include: 

1. Security vulnerabilities in generated code

AI-generated code can look correct but still lack secure patterns. 
For example, it may miss input validation, meaning the system does not properly check what data it accepts. This can expose it to common attack methods, often referred to as OWASP Top 10 risks (see: OWASP GenAI Top 10). 

2. Prompt injection

Prompt injection is a technique where malicious input is designed to manipulate an AI system. 
Instead of attacking the code directly, the attacker tries to influence the AI so that it leaks sensitive data or performs unintended actions. 

3. Model poisoning

Attackers introduce malicious or misleading data into the datasets used to train AI models. 
As a result, the AI may produce incorrect, biased, or insecure outputs, even if it appears to function normally. 

4. Slop squatting

Slop squatting builds on the idea of typo squatting. 
Instead of relying on human typing errors, attackers exploit AI hallucinations, situations where AI generates plausible but incorrect package names or dependencies and create malicious versions of those resources. 

 
Not just threats, real opportunities 

It is important to emphasize that this is not just a story about risk. 

The same capabilities that introduce new challenges also unlock significant opportunities. 

We are already seeing: 

• Improved vulnerability detection 
  AI can identify issues in codebases faster and at a larger scale than traditional approaches 

• Self-healing 
  Systems that can detect and fix issues automatically without human intervention 

• AI-driven code review and adversarial testing 
  One AI system tests and challenges another, improving overall robustness 

These capabilities are already being explored in practice, read more about it in our blog on SmartGen AI Suite.

In practice, this can lead to better security outcomes, especially for organizations that adopt these capabilities early and responsibly. 

 
A familiar turning point 

Looking ahead, this moment feels like a technological crossroads. 

It is not identical to previous shifts like Y2K, but the sense of urgency is similar. Organizations face a fundamental choice. 

They can wait and see what AI-driven tools will reveal about their vulnerabilities. 
Or they can act and adopt an AI-first approach that improves both security and development speed (see: Application Development services page).

This time, AI is not just exposing problems. It is also a key part of the solution. 

 
Key takeaways & final thoughts 

- AI is reshaping software development at an unprecedented pace   

- It introduces new security risks that organizations must actively manage   

- Proven principles like zero trust and defense in depth remain essential   

- At the same time, AI creates new opportunities to improve security and efficiency  

AI in software development is neither purely a threat nor purely an opportunity. Its impact depends on how it is adopted.   

Organizations that combine AI capabilities with strong security fundamentals are best positioned to move faster without compromising trust.