By Akinsola, JET Ph.D.
Our digital defences are only as good as the people who use them. Artificial intelligence may finally make them easier to use.
Most of us have clicked on something we should not have. It might have been a message that looked like it came from the bank, a login page that seemed exactly right, or a link that promised a prize. Security experts have a name for the person on the receiving end of such traps: the weakest link. A research chapter by a group of Nigerian academics argues that we have been blaming the wrong party. Often, they suggest, the problem is the screen in front of us.
Know your enemy on paper
Before an organisation can defend itself, it must imagine how it might be attacked. That exercise is called threat modelling. Security teams list what they own, work out who might attack it and how, rank the dangers by severity, and decide where to spend limited time and money. Done regularly, it keeps defenders a step ahead of emerging threats.
The trouble is that traditional threat modelling can be slow, manual and hard to use. The chapter counts more than a dozen methods, from Microsoft’s pioneering STRIDE, dating to 1999, to others known by acronyms such as PASTA and DREAD. Each has strengths. STRIDE suits teams with modest security expertise, while PASTA is more demanding but more thorough. No single standard exists, and the right choice depends on the project’s risks, timeline and available skills.
The interface problem
Every security tool is ultimately operated by a human being, and the place where human and machine meet, the interface, shapes whether people use the tool well. A cluttered dashboard that throws raw numbers at an operator is a security risk in itself, and so is a warning that no ordinary user can interpret.
Good design, they argue, starts with the person. It asks what the user is trying to do, how they take in information and make decisions, and whether they can see what the system is doing at any moment. Trust matters too. People hand over card details only to sites they trust, and a design that squanders that trust invites trouble. Too many systems, the authors say, are built around the technology rather than the user.
Interfaces that think
Enter what researchers call the intelligent user interface, or IUI, a screen that does more than display. It watches, learns and adapts. Gmail’s Smart Compose, which finishes your sentences, is a familiar example. So are voice assistants like Alexa, the recommendations on Netflix or Spotify, and the chatbots that greet you on company websites. An ordinary interface behaves identically for everyone, but an intelligent one adjusts to the person using it, and can step in when that person is about to go wrong.
These interfaces can also read more than keystrokes. They can respond to voice, hand gestures, gaze and even mood. A help-desk system, for instance, can detect a frustrated caller and route them to a better channel. Behind all this sits machine learning, which lets software spot patterns in past behaviour and predict what comes next.
Catching the con
Phishing offers the clearest illustration. Many fake websites are near-perfect clones of real ones, and attackers adapt quickly whenever defenders catch up. The authors point to the promise of an interface that does more than flash a red warning. It would tell you a site is fake and explain why, so that people learn to spot the signs themselves. Each time the system protects someone, trust in it grows, and a trusted tool is one people actually rely on.
Real products already work along these lines. Some security platforms learn how people normally behave on a system and flag oddities, while others protect the interface itself from tampering. The same logic applies to spam filtering, malicious traffic and the detection of fake news.
Faster design
Artificial intelligence is not only inside the tools but also helps build them. Designing an interface has traditionally meant sketching on paper, redrawing the sketch as a digital wireframe, then having developers turn it into working code. The loop is repetitive and slow. Tools such as Uizard can now take a hand-drawn sketch and produce a working prototype. The authors list several benefits: quicker iteration, more time for designers to focus on what matters to users, and a lower barrier for people without coding skills, since anyone can draw a screen on paper.
Four rules for building a secure interface
There are four baseline principles for AI-enabled interfaces, and they read like common sense that is too often ignored. Be honest about what the tool can and cannot do, so users do not expect miracles. Design for forgiveness, because the AI will make mistakes. Be transparent about the data collected, and let users adjust it. And never compromise privacy, security or the user’s control.
The catch
No technology comes without costs, and the authors are candid about them. Intelligent interfaces are harder to evaluate, cost more to build and run, and depend on good data and computing power. Designers must therefore weigh whether the added complexity is worth it for a given tool.
The same technology also serves the other side. Criminals use AI too, and AI systems can themselves be attacked. In data poisoning, training data is quietly corrupted, and in model evasion, inputs are crafted to slip past a detector. The authors see this as a reason for good security practice and real expertise, not for holding back.
The takeaway
For anyone running an organisation, the lesson is practical. Spending on security software is not enough. The people who operate it, and the screens they use, deserve equal attention. Ask whether your tools explain themselves, whether they adapt to the person using them, and whether staff trust them enough to act on their warnings.
The old saying holds that a chain is only as strong as its weakest link. The cure is to build tools that help people become stronger links, rather than to blame them for being weak ones.
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