👋 Welcome to Tami’s Technical Enablement newsletter. Your weekly guide to scaling AI, software engineering, and tech talent.
In last week’s newsletter, we discussed how CTOs and enterprises are quickly embracing vibe coding. There are clear benefits, primarily in the speed at which you can launch an application. However, they also carry risks, especially for enterprises and CTOs.
“You need to challenge AI’s answers to find the correct one. Blindly trusting AI can lead to system crashes and leave many developers completely stuck or lost.”
In this week’s newsletter, we’ll cover:
Why AI-generated code can be vulnerable to cyberattacks
Why you shouldn’t trust AI-generated code too much
The risk of shadow AI code development
How to balance speed and security
How to learn AI and cybersecurity
Why AI-Generated Code Can Be Vulnerable To Cyberattacks
AI-driven vibe coding tools often automatically import external software components, but these aren't always carefully vetted, posing serious business risks.
Some components can even be malicious, leading to data breaches, system failures, or costly downtime if incorporated.
Studies estimate nearly 48% of AI-generated code snippets contain exploitable vulnerabilities, highlighting security concerns.
Security teams now face increased pressure to address the risks associated with AI coding.
Plus, vibe coding enables non-technical users, such as business managers and marketers, to develop apps using AI tools.
However, many of these users lack cybersecurity training, which leads to skipping critical safety steps.
Why You Shouldn’t Trust AI-Generated Code Too Much
Problems arise when teams overtrust AI-generated code, thinking it is safe just because a machine created it.
There are many reasons to be skeptical of using AI-generated code in production:
Security vulnerabilities:
As mentioned above, AI models can introduce security vulnerabilities like insecure dependencies, buffer overflows, memory leaks, access control issues, or flaws in authentication mechanisms.
Research suggests a significant portion of AI-generated code contains security bugs, and developers using AI assistants may be more likely to believe their code is secure, increasing the potential for problems.
Potential for errors and bugs:
AI can generate logically incorrect, inefficient, or unnecessarily verbose code.
Debugging AI-generated code can be more challenging because the AI doesn't explain its choices, making it harder to trace the root of the problem.
Technical debt:
AI-generated code may function effectively but may not be optimal for long-term maintenance, readability, or scalability.
Over time, this results in increased technical debt.
Hallucination risk:
AI can make mistakes. It might become biased, confused, or inefficient depending on the task.
Known as hallucination risk, AI can get stuck in a loop, drift into unrelated parts of the codebase, suggest irrelevant changes, or output plausible but incorrect results.
Without human review and continuous training, these threats can go unnoticed, endangering the entire business.
The Risk of Shadow AI Code Development
Shadow AI refers to the use of artificial intelligence tools and applications by employees without formal approval or governance from their IT departments.
Much like shadow IT, shadow AI specifically involves generative AI models, agents, copilots, tools, and other AI systems that haven’t undergone proper security vetting processes.
The amount of code that organizations can generate with AI assistance is rising rapidly and could soon account for most of all production code.
The stakes are especially high for complex enterprise applications, where a single hallucinated dependency can lead to catastrophic failures.
AI-generated code and non-technical users can also result in unpredictable costs and financial risks.
“The types of failures we’re seeing aren’t just bugs — they’re architectural failures that can bring down entire systems.”
How To Balance Speed And Security
Engineering leaders face a crossroads as AI-enabled "vibe coding" reshapes the software development landscape.
The convenience and speed are undeniable, as are the hidden cybersecurity and financial risks that accompany them.
To protect your organization, take these proactive steps:
Prioritize employee education about AI and security.
Mandate human code reviews for all AI-generated outputs.
Establish clear policies for AI code adoption and escalation protocols.
Embed security checks throughout your software development life cycle.
How To Learn AI and Cybersecurity
There are many ways to learn about AI and cybersecurity.
For individuals, traditional methods like reading and self-paced courses are inexpensive but lack interaction or expert guidance.
Public instructor-led classes are available, but they tend to be introductory or standard courses and open to everyone.
You can also get instructor-led training from a training company, but this can take months to coordinate.
Now, you can try Tami: get expert-led, customized training for your team or organization, delivered in just days when you need it, and automate all logistics to scale training faster.
Learn more by requesting a demo with our founders.
Thank you for reading!
