AI is writing more of your store’s code every single day. Product recommendation logic, checkout tweaks, plugin customizations, internal tools. A developer describes what they want, the AI writes it, and it ships. That part everyone already knows. What most store owners have not connected yet is the other half of that story. Nobody is checking that code as fast as it gets written. That gap between how fast code gets created and how carefully it gets checked is where your store’s next breach is most likely to come from. I run an AI and cybersecurity firm, and I want to walk you through exactly why this gap exists, what it actually costs a growing store, and what closing it looks like in practice, without the jargon that usually gets used to describe it.
The uncomfortable math on AI written code
I am not going to guess at numbers here, because guessing is exactly the problem I am describing. Independent research has actually measured this, and the results should get your attention. A study from Veracode found that 45 percent of AI generated code contains security flaws. Separate research cited by the Cloud Security Alliance found that 62 percent of AI generated code solutions contain design flaws or known security vulnerabilities. And research summarized by SoftwareSeni found that AI generated code contains 2.74 times more vulnerabilities than code written by a human developer.
Read those numbers again slowly. Nearly half of AI generated code has a real security flaw in it. Almost two out of every three AI built solutions carry a design flaw or a known vulnerability. That is not a rare edge case. That is close to a coin flip every time a piece of code gets generated and shipped without a real review behind it.
None of this means AI is bad at writing code. It writes working code very well. Working and secure are two different things, and that difference is exactly what gets missed when a team is moving fast and trusting the output because it runs without errors. Code that runs is not the same as code that is safe.
45%
of AI generated code contains a security flaw
Veracode, 2025
62%
of AI built solutions carry a design flaw or known vulnerability
Cloud Security Alliance, 2025
2.74x
more vulnerabilities than human written code
SoftwareSeni, 2025
Why this hits online stores harder than most businesses
Every business that writes software carries some version of this risk. Online stores carry more of it, for a simple reason. Your site is not just a website. It is a payment system, a customer database, and a public facing application that strangers interact with every hour of every day, including hours when nobody on your team is watching.
Think about what actually runs through a typical e-commerce checkout flow. Card numbers, billing addresses, saved payment methods, customer account credentials, order history tied to real names. A flaw in a marketing plugin is annoying. A flaw in the code path that touches any of that data is a different category of problem entirely, and it is exactly the kind of code most stores are generating and shipping the fastest, because checkout experience and personalization features are where the pressure to move quickly is highest.
Stores also get targeted more than most people realize, and not because attackers pick favorites. Automated scanners crawl the internet constantly looking for known weaknesses, and they do not care whether your store does ten sales a day or ten thousand. They care whether a door was left open. A store spinning up new AI generated features every week, without a matching increase in how often that code gets checked, is opening new doors faster than it can lock the old ones.
The kind of mistakes AI quietly makes
It helps to know what these flaws actually look like in practice, because “security vulnerability” is a vague phrase until you see the shape of it. Here are the patterns that show up again and again in AI generated code for web applications.
Input handling that trusts the user too much. When code accepts something a customer typed, whether that is a search box, a coupon field, or a shipping address, it needs to check that input carefully before doing anything with it. AI generated code frequently skips or weakens that check, which opens the door to attacks like SQL injection, where someone manipulates a form field to make your database reveal or destroy data it was never supposed to expose.
Weak login and account protection. AI generated authentication code has a documented pattern of using outdated password storage methods and skipping basic protections like locking an account after repeated failed login attempts. That sounds small until you remember this is the exact code standing between a stranger and your customer accounts.
Borrowed components nobody vetted. AI tools frequently recommend third party libraries and plugins to solve a problem quickly. Convenient, but those recommendations are not always current, maintained, or safe. A developer moving fast can install a suggested package without checking whether it has known vulnerabilities or whether anyone is still maintaining it at all.
None of this is because the AI is trying to cause harm. It is because these models learned from a massive pool of publicly available code, and a meaningful share of that code carries the same flaws. The AI does not know your store’s specific risk profile, your compliance requirements, or what a bad outcome looks like for your specific business. It only knows patterns. Some of those patterns are bad ones.
What a breach actually costs a growing store
Store owners tend to treat security spending as optional because the cost of skipping it feels abstract, right up until it is not. The Verizon Data Breach Investigations Report, one of the most cited security research reports in the industry, put the typical cost range for a small or midsize business incident at 120,000 to 1.24 million dollars in 2025. That range covers the real, unglamorous work of dealing with a breach. Hours spent by your team instead of running the business. Specialists brought in to figure out what happened and close the hole. Customers who leave and do not come back. Higher insurance costs afterward. Possible penalties if payment data was involved and your compliance obligations were not met.
Put that number next to what a real testing program costs, and the math stops being a close call. A structured, ongoing review of your store’s code and infrastructure costs a fraction of even the low end of that range. Store owners who skip it are not saving money. They are deferring a much larger, much less predictable bill and hoping it never arrives.
Knowing what your AI tools can actually touch
Here is a question I ask every new client, and most cannot answer it on the spot. What can your AI coding tools actually reach. Not what you intended them to reach. What they are actually configured to access right now, today.
AI coding assistants and automation tools often get connected to a store’s codebase, database, or hosting environment with broad permissions, because broad permissions are the fastest way to get a tool working and nobody goes back afterward to narrow them down. That convenience creates a quiet, growing blind spot. If a tool with that kind of access is ever compromised, or simply makes a mistake that a human would have caught, the blast radius is defined by what it could reach, not by what you meant for it to reach.
Fixing this does not require abandoning AI tools. It requires treating their access the same way you would treat a new employee’s access. Give them what they need to do the job in front of them, nothing more, and review that access on a schedule instead of setting it once and forgetting it exists. Most stores I have reviewed have never done this basic inventory. It takes an afternoon and closes a gap most owners did not know they had.
Why once a year testing does not cut it anymore
A lot of store owners think of security testing the way they think of a fire inspection. Something you schedule once a year, pass, and file away. That approach was already outdated before AI generated code became common. Now it is genuinely dangerous.
Annual testing made sense when a store’s codebase changed slowly. A test done in January was still a reasonably accurate picture of the store in October. That is not true anymore for a store shipping new AI assisted features every week or every month. A single test, done once, is a snapshot of a store that no longer exists by the time the next major feature ships.
This is not just my opinion. Under PCI DSS version four, the compliance standard that governs how businesses handle payment card data, organizations are required to conduct penetration testing at least annually and after any significant change to the systems that handle cardholder data. Read that second part carefully. After any significant change. A new checkout flow, a new customer portal, a new integration, a new authentication method. If your store is shipping changes like that regularly, and most growing stores are, the compliance bar itself is telling you that annual alone is not enough.
The practical rule I give clients is simple. Testing frequency should track your rate of change, not the calendar. If your store ships major changes to checkout, login, or customer data handling every month, your testing cadence needs to be closer to that rhythm, not stuck on a once a year cycle that was already too slow before AI made code generation this fast.
The system cannot fairly check its own work
Here is a problem that is easy to miss if you are not looking for it. A lot of teams now ask the same AI that wrote a piece of code to also check that code for problems. That sounds efficient. It is also a conflict of interest built directly into the process.
Think about what you are actually asking. The model generated the code based on patterns it learned. When you ask it to review that same code, it is checking its own output against the same patterns it already used to write it. If those patterns contain a blind spot, the review inherits the same blind spot as the code. It is not lying to you. It genuinely does not see the problem, because the problem is baked into how it thinks about the task in the first place.
This is exactly why independent testing matters more now, not less. A separate process, separate tooling, and ideally a separate set of eyes that were not involved in writing the code in the first place. Not because the developer or the AI did something wrong on purpose, but because nobody, human or otherwise, catches every blind spot in their own work. That is not a character flaw. That is just how blind spots work.
What continuous testing actually looks like for a growing store
Continuous testing sounds like it might mean an expensive, constant process running in the background of every store. It does not have to. For most growing stores, it means a few concrete habits, done consistently, instead of one big test done once a year and forgotten. If you have not already, it is worth reading through the broader mistakes I see growing stores make on security, since code level testing is only one piece of that picture.
Every significant change gets reviewed before it goes live, not after. A new checkout feature, a new AI generated recommendation engine, a new customer facing tool. The review happens as part of shipping it, not as a cleanup exercise months later when something has already gone wrong.
Code that touches customer data gets a higher bar than code that does not. Not everything needs the same level of scrutiny. A cosmetic change to a product page banner is not the same risk category as a change to how the site stores or processes payment information, and your testing effort should reflect that difference honestly instead of treating every deploy the same.
Dependencies get checked on a schedule, not just when something breaks. The plugins and libraries your store relies on get new vulnerabilities disclosed against them all the time. A library that was safe when you installed it can become a liability eighteen months later without a single line of your own code changing.
Someone independent looks at the whole picture periodically. Not the same person or system that built the feature. A fresh set of eyes catches things that familiarity makes invisible to the person who has been staring at the same code for weeks.
None of these four habits require a large team or a large budget. What they require is a decision to treat security testing as part of how your store ships software, not as a separate project that happens occasionally when someone remembers to schedule it. The stores that get this right are rarely the biggest ones. They are the ones where somebody made testing a routine instead of an event.
What I would check first if I inherited your store’s codebase today
If you handed me your store tomorrow and asked where to start, here is the order I would actually work in, because some of these matter more than others in the first hour.
Every place customer input reaches your database. Search boxes, coupon codes, contact forms, address fields. Anywhere a stranger can type something and your system does something with it is worth checking first, because input handling flaws are some of the most common and most damaging issues in AI generated code.
Every AI assisted feature shipped in the last six months. Not because AI assisted code is automatically bad, but because it is the code least likely to have had a proper independent review, given how fast it typically moves from idea to production.
Every third party plugin and library currently installed, cross referenced against known vulnerability databases. This takes an afternoon and regularly turns up something nobody remembered installing, let alone checking.
Login and account security specifically. Password storage method, account lockout behavior, and whether administrative accounts have any extra protection beyond a password alone. This is consistently one of the weakest points in stores I have reviewed, AI generated code or not.
None of this requires exotic tools or a massive budget. It requires someone who knows what they are looking for, doing it deliberately, instead of assuming that code which runs without errors is code that is actually safe.
Where Globotech Solutions fits into this
This is the exact gap Globotech Solutions was built to close. I run Globotech as an AI and cybersecurity firm precisely because these two things cannot be treated as separate departments anymore. A team that only knows how to build AI features and a team that only knows how to test for vulnerabilities will always be one step behind a threat landscape that combines both. Ours does not work that way.
When we work with an online store, the review is not a one time event we bill for and disappear after. We look at what is actually shipping, how fast it is shipping, and where the testing cadence needs to sit relative to that pace, using the certifications and methodology I have built this career around, including CEH Practical, CompTIA Security+, and CompTIA CySA+. If your store is generating code faster than anyone is checking it, that is not a future problem. Based on the research above, there is a real chance it is already a current one.
What actually matters here
AI writing your store’s code is not the threat. Trusting that code without checking it, at the same pace it gets written, is the threat. The numbers are not close. Close to half of AI generated code carries a real flaw, annual testing was already too slow before this shift and is worse now, and the same system that wrote a piece of code is a poor judge of whether that code is actually safe.
Fix the pace mismatch and you fix most of this. Match your testing rhythm to your shipping rhythm. Give customer facing and payment adjacent code a higher bar than everything else. Bring in an independent set of eyes on a real schedule, not just when something already feels wrong. That is the whole difference between a store that is exposed and a store that is actually ready for how fast software gets built now.
None of this requires you to slow down how fast your store ships new features. AI is not going anywhere, and it should not be. It does mean applying the same scope discipline that decides whether an AI feature ships at all, and being honest about what shipping fast actually means today, and to put a real testing rhythm behind it instead of assuming speed and safety take care of themselves. They do not. They never did, not for any business at any point in the history of software. AI just made that old gap between speed and safety show up faster and more often than it used to.