With AI tools, it is easier than ever to create an app, website, or internal tool without hiring a development team. But the ease of construction does not always translate to the ease of operation. In this blog, we examine the true risks that businesses face by deploying AI software without adequate security review, and how quick isn't worth sacrificing protection.
Introduction
Several years ago, it was a usual practice to build customized software by hiring software developers, drafting specifications, and waiting for the software to be built and ready to work for weeks or months. Today, AI can create a working app, website or internal system in a few hours (and sometimes minutes). It's an impressive one, and for some small businesses and entrepreneurs, it's been the key that unlocked the door they couldn't afford or have lacked the technical know-how to open.
There is one exception though, that is easy to overlook in all the excitement, "fast" does not equal "secure. But when it comes to software that deals with the customer's data, payments, or business operations, that difference can cost and it can cost a lot.
It Works is not necessarily It's Safe.
AI-generated code may seem sleek and logical at first glance: buttons move, forms are submitted; dashboards load. Of course, normal use does not reveal the vulnerabilities. They appear when someone attempts to break into the house on purpose.
Common problems with AI software are weak authentication, unencrypted data storage, lack of proper input validation, and so on all of which are things a trained developer should be able to spot, but can easily overlook if the software is built to perform a function rather than to be safe.
The hidden costs of doing things on the cheap.
A security breach isn't just a technical issue for a business, it can be a financial and reputational problem. The consequences of a data breach could be costly, with fines from regulators, loss of customer confidence and a lack of time spent on careful planning around emergency fixes. The time spent fixing a broken reputation is frequently much greater, and far more time consuming than it would have taken to build the software quickly in the first place.
Then there's the fact that the legal issues are there. When a business gathers customer information, such as names, e-mail addresses, payment information, they typically have a legal responsibility to keep that data safe. A "made by the AI" excuse doesn't work when things go wrong.
When AI-Built Software is Appropriate.
All of this does not mean that the use of AI should be completely prohibited in the development process. If you're working on an AI-generated tool for internal use, prototypes, or personal projects, or if the software doesn't interact with sensitive data, AI code can be a real time-saver. The risk increases as the software is used and as the user base increases.
So the question is not whether or not to use AI to create this, but whether or not there is anything in this software that's sensitive enough that it needs a security review.
A guide to the responsible use of AI-created software.
- Keep prototypes away from production. Do not hesitate to use AI for playing around and testing out concepts, but do not treat it the same way if it's for the customer or if it involves data.
- Have a security review prior to launch. Before deploying AI-generated code, a developer or security expert can manually inspect the code for potential problems that the AI might not have identified.
- Keep the software's reach to a minimum. The less sensitive the data is that the tool processes, the fewer the consequences for any missteps.
- Keep Dependencies and Libraries up to date. Vulnerable or outdated packages are sometimes imported by an AI tool without indication.
- Have a plan for ongoing maintenance. Once it's launched, software is not finished, there are vulnerabilities over time coming to light, and someone has to be accountable for fixing them.
The Bigger Picture
In the real world, artificial intelligence has truly become a game-changer for companies that lack extensive IT support teams. What's worked best, however, are the businesses that embrace AI as a catalyst for creation, not as a shortcut to the judgment, review, and accountability that was traditionally built-in with professional development.
Speed is valuable. However, when this software is interfaced with real customers and real data, a bit of friction at the beginning is almost always more economical.
FAQs
1. Does the AI software have a lower security level than the software that is professionally made?
Not per se, but by default, AI tools don't focus on security, focusing on functionality instead, so vulnerabilities can easily be overlooked if they are not the priority when reviewing the code.
2. Can AI become a safe solution to create an internal company tool?
Yes, in most situations for internal tools, but this may become an issue if the tool accesses customer information, payment information, or important business data.
3. What is the biggest risk associated with not conducting a security review on software created with Artificial Intelligence?
Lack of encryption or inadequate authentication may result in data breaches with financial, legal, and reputational implications.
4. Do small businesses have the budget for an AI security audit of their software?
Yes, a one-off audit is often much cheaper than if there's a breach, and it's usually available for smaller projects at affordable rates for the developer.
5. If there are mistakes, does it mean that the use of AI for software development absolves the company of legal liability?
Yes, businesses are still accountable for ensuring that customer data is safe, even if the software is software-as-a-service, or even if it's created using AI tools.
