AI-powered apps are rapidly being adopted and used by a growing number of organizations and businesses. We now have recruitment tools, health apps, fintech dashboards, and AI-based chatbots to serve customers with confidence.
As a founder, business owner, or manager, there’s a good chance you’re already using or developing one of these apps. However, as this rapid development continues, there are many risks to consider.
As apps operate independently and make decisions without human supervision, security issues become different from traditional bugs.
Instead, the problem is the silent leakage of sensitive information due to biased results or faulty functioning of algorithmic models, which can later cause significant harm.
That’s why humans remain in charge of effective and safe AI-assisted coding. Although automated processes improve the efficiency of the development process, developers are ultimately responsible for ensuring the security of their AI systems.
Why AI application security requires human oversight
AI makes decisions through predictions based on previous datasets. This works well until it’s implemented in the real world when dealing with customer data, finances, and trust.
Cybersecurity teams are already facing different types of problems related to AI applications, including leaking training data through prompts, models being tricked into performing potentially unsafe actions, and biased output due to thought patterns built into the AI design.
Additionally, fully automated security measures can miss context. Therefore, it is up to the human user to determine if the situation is unusual or suspicious.
The difference between understanding these issues and ignoring them is why monitoring is important. Monitoring should be more than just a checkbox, it should be a habit that business owners put in place to protect against cyber-attacks.
Understand the security risks specific to AI applications
AI apps are susceptible to many risks that don’t affect traditional apps.
- Immediate injection: Here, end users manipulate the desired behavior of the trained model to leak sensitive information or break certain guidelines.
- Model addiction: When the training information for an AI model changes, changing the way the model responds or reducing the accuracy or reliability of the model.
- Data drift: This happens when the training provided to the model becomes less effective over time and no one notices this change sooner.
- Misuse of the model: When a tool is developed to solve a specific problem and then used in ways not intended by the developer.
Security scans of most older apps will not reveal the risks listed above. So you need someone who has a good understanding of the intent of the system itself, not just the endpoints.
Raising awareness of these issues is essential before implementing any controls to protect your business, stakeholders, and customers.
Human involvement as a core security principle
Human participation means that the model does not make the final decision. people do that. Human involvement includes reviewing procedures for sensitive output, providing approval for actions that impact users, and validating alerts from machines to limit the risk of misinformation.
This reduces false alarms and, more importantly, eliminates the possibility of overconfidence errors by the AI. There is ample evidence that mistakes in AI models have negative real-world consequences.
For example, Amazon stopped using an AI recruiting tool after people perceived it to be biased against resumes that included words like “female.” Additionally, Microsoft’s Tay bot collapsed hours after it was launched as it began producing offensive content without human supervision.
The machine will not learn the limits unless a human draws them.
Governance, Ethics, and Accountability in AI Security
AI will definitely make things easier, but it will also hold you accountable if it fails. Human oversight of some high-risk AI systems is a requirement of EU AI law.
This includes oversight by a designated individual or entity responsible for ensuring that AI systems are developed, implemented, and evaluated responsibly. This person or entity must be responsible for what the AI system produces.
Ethical concerns such as bias, privacy, and fairness are human responsibilities. To ensure that AI aligns with real-world rules, clear ownership, roles, and escalation paths must be established. This cannot be achieved with training data alone.
A good governance setup answers three simple questions: Who can override the AI? Who will review its behavior over time? Who will take responsibility if it goes wrong?
Combining automated defense and human judgment
Unlike humans, who can only see a small number of records at a time, AI doesn’t get tired of looking at logs. This allows businesses to use AI to monitor data, identify anomalies, and take action if they occur.
Using AI models, enterprise security personnel spend less time investigating and can instead focus on correctly responding to incidents with fast and accurate responses.
The speed of AI log monitoring combined with human expert input allows businesses to not only respond quickly, but also control the speed at which corporate services and networks are shut down.
The fact that well-designed AI systems can provide real-time log analysis and support human decision-making is demonstrated, among others, by Seceon.
Secure AI systems rely on human leadership
While AI allows us to create smarter applications at a faster pace and at a lower cost, it does not replace human judgment or make us legally responsible for our actions.
Additionally, when the user requests an answer, it does not explain itself. Therefore, it is the developer’s responsibility to control its use, perform audits, and discontinue its use if necessary.
The future of security is for security professionals to use AI as a tool to support their professional judgment, rather than AI operating in isolation.
In other words, AI does not have unlimited privileges. Organizations that learn how to effectively deploy and manage AI can build and maintain customer trust.
