Human-Led Security in an AI-Driven Application World

AI-powered apps are rapidly being implemented and utilized by an ever-increasing number of organizations and businesses. We now have hiring tools, health management apps, fintech dashboards, and AI-based chatbots providing customer service with an air of self-confidence. 

There is a strong possibility that, as a founder, business owner, or manager, you are already using or even developing one of these apps. But as this speedy development continues, there are a lot of risks that need to be considered.

When apps begin operating independently and making decisions without human supervision, security issues no longer look like traditional bugs.

Instead, it becomes about the silent leaks of sensitive information due to biased outcomes or an incorrect functioning of an algorithmic model that later culminates in substantial harm. 

That’s why humans continue to be responsible for effective and secure AI-assisted coding. Automated processes increase the efficiency of the development process, but ultimately, the developers remain accountable for securing their AI systems.

Why AI Application Security Requires Human Oversight

AI makes decisions via predictions based on previous data sets. This works well until they are implemented in the real world when dealing with customer data, finances, and trust.

Cybersecurity teams are already encountering different kinds of issues related to AI applications, including training data leaking through prompts, models being tricked into performing potentially unsafe actions, or biased outputs due to patterns of thought that have been built into the AI design.

Additionally, fully automated security measures sometimes miss context. Therefore, it is up to the human user to determine if a situation appears unusual or suspicious.

The difference between understanding these issues and ignoring them is why oversight is important. Oversight should not just be a checkbox, but a habit that business owners put in place to protect against cyberattacks.

Understanding Security Risks Unique to AI Applications

AI apps are susceptible to many risks that do not affect traditional apps:

• Prompt Injection: This is where the end-user manipulates the desired behavior of the trained model to divulge sensitive information or break specific guidelines.
• Model Poisoning: When the training information for the AI model has been altered to change how the model responds or make it less accurate and reliable.
• Data Drift: This occurs when the training provided to the model becomes less effective over time, and no one notices this change early.
• Model Misuse: When a tool is developed to solve a specific problem and is later used in ways never intended by the developer.

Most old app security scans do not reveal the risks listed above. Therefore, you need someone with a good understanding of the intent of the system itself, not just its endpoint.

It is imperative to raise awareness of these issues before any controls can be instituted to protect your business, stakeholders, and customers.

Human-in-the-Loop as a Core Security Principle

Human-in-the-loop means the model doesn’t get the final word; a person does. Human involvement can include reviewing steps for sensitive outputs, providing approval on user-impacting actions, or verifying alerts from the machine to limit the risk of misinformation.

This leads to reduced false alarms and, more importantly, eliminates the possibility of overconfident errors made by the AI. There is ample evidence of the negative impact of AI model mistakes in the real world.

For example, Amazon stopped using its AI hiring tool after people identified some biases against resumes with words like “women’s.” Also, hours after launching, Microsoft’s Tay bot collapsed because it began producing offensive content without human supervision.

Machines don’t learn limits unless people draw them.

Governance, Ethics, and Accountability in AI Security

AI definitely makes things easier, but when it fails, you’ll be held accountable. Human oversight of some high-risk AI systems is a requirement of the EU AI Act.

This includes oversight by an appointed individual or entity who will be responsible for making sure that AI systems are responsibly developed, implemented, and evaluated. This person or entity has to take responsibility for what the AI system produces.

Ethical concerns, such as bias, privacy, and fairness, are the responsibility of humans. Clear ownership, roles, and escalation paths must be established to ensure AI aligns with real-world rules. This cannot be achieved with just training data.

A good governance setup answers three simple questions: Who can override the AI? Who reviews its behavior over time? Who’s accountable when it’s wrong?

Combining Automated Defenses With Human Judgment

AI doesn’t tire from looking at logs, unlike humans, who can only look at a few records at a time. This allows companies to use the AI to monitor data and identify anomalies, then act on them as they arise.

Using an AI model allows a company’s security personnel to spend less time investigating and instead focus on correctly responding to incidents with quick and correct responses.

By combining the speed of AI log monitoring with human expert input, companies can still respond quickly but also retain control over how quickly they shut down a company’s services or network.

The fact that well-designed AI systems can provide real-time log analysis and support human decision-making is demonstrated by Seceon, among others.

Secure AI Systems Depend on Human Leadership

While AI allows us to create applications at a faster pace, with less expense and also smarter, it does not replace human judgment, nor will it assume legal liability for one’s actions.

Furthermore, when users demand answers, it doesn’t explain itself. As such, it is the responsibility of the developer to manage its use, perform audits, and discontinue use if necessary.

The future of security is not AI operating independently, but rather security professionals using AI as a tool to support their professional judgment.

So, AI doesn’t have unchecked authority. The organizations that learn how to effectively employ and manage AI will build and retain the trust of their customers.