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Can Artificial Intelligence Detect Business Logic Attacks Early?

Advanced application security solutions use AI to detect not only a wide range of hacking exploits but also often overlooked business logic attacks.

If you’re developing an application or an e-commerce website, you know that each process follows a set of rules.

For example, when a customer places an order, the site will calculate the cost, discounts, shipping, and taxes. Then, it has to update the user’s account and notify them that their order is processing or being shipped.

This is known as business logic.

Cybercriminals who are familiar with the exact processes of your company can exploit them to obtain sensitive data of your company or make changes in algorithms.

Unlike traditional attacks, such as malware, DDoS, or phishing threats, business logic attacks exploit insecure APIs and non-technical flaws within applications.

BLA is a cyber attack that’s challenging to detect with traditional security tools. It can bypass the API security you have or the firewall that you set up to protect your application.


When a bad actor compromises an application with BLA, security tools may not register it at all. They’ll perceive it as if the app is undergoing regular business logic processes.

Could Artificial Intelligence (AI) change that?

How do security tools that rely on AI discover and stop business logic attacks before they disrupt your company’s processes?

Let’s find out.

Identifying Anomalies Within the Application

Unlike well-known types of malware or other exploits that hackers use to compromise networks and apps, business logic attacks don’t follow a specific pattern cyber tools could detect to mitigate threats. The attack surface of applications is continually shifting as well.

Tracking all changes manually is both unsustainable and time-consuming.

This is where AI makes a difference. It can gather a large amount of data about behavior within the app and analyze its findings 24/7.

Since it continually gathers data, it can alert security teams as soon as it detects something that stands out — an anomaly.

For example, it can rely on the User and Entity Behavior Analytics that monitor how the users usually use the app. AI is used here to form user profiles and learn how they normally use the system.

After tracing the data about the business logic and users after some time, AI can spot anomalies. Those could be tweaked authorizations or changes in when and how (by whom) the purchase is processed within the software.

Enforcing Rule-Based Principles

In addition to collecting and analyzing data to provide the teams with alerts, AI can also help enforce rules that safeguard your app when it’s undergoing a suspected business logic attack.

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Since every organization operates based on different business logic, every company can set its own custom rules to define what is acceptable and not within the system.

Some of the criteria they can consider are compliance, overall top security practices, or protective policies the business already has.

AI can be used to ensure that these rules are applied across the entire software environment. It decides whether to take action based on the insights or send out an alert.

Sometimes, this means that the attempt to access a certain account will be blocked right away.

At other times, security teams will receive an alert that they need to investigate to uncover and mitigate advanced threats.

Protecting an App With Advanced Application Security

The cybersecurity solution that you set to prevent attacks on your application or website should be capable of not only detecting bot attacks but also spotting broken authorization, and business logic attacks.

The best way to fight business logic attacks is with comprehensive security that takes into account your entire attack surface. And then uses AI to analyze security data as well as to block the threats and suspicious behavior right away.

The advanced application security solution you choose to safeguard your apps and websites should be capable of:

  • Taking into account the ever-shifting attack surface
  • Not affecting the regular conduct of your business
  • Helping you manage a large number of alerts

Responding to Potential BLA Attacks in Real Time

Together with continual data gathering and analysis of user behavior, AI can also help you respond to threats (detected anomalies) in real time.

This is integral for application security because the longer you wait to discover cyber-criminal activity within your system, the more damage they do to the finances and reputation of a business.

The average cost of a data breach for U.S. companies that have experienced it in 2023 was 9.48 million U.S. dollars. Unfortunately, many never recover from that.

Whether we discuss ransomware or insider threats, hacking activity is a time-sensitive operation. It requires an immediate response — especially when it can endanger assets such as personal data.

Can AI Fight Business Logic Attacks?

AI can be used to detect behaviors that indicate a business logic attack is taking place within your app or website. Every company has a different set of rules and processes. AI can keep track of them and respond to anomalies on time.

Business logic attacks are possible when a threat actor knows the software inside and out. For instance, they may know the exact time when purchasing orders are processed by an e-commerce company.

These attacks affect not only your inner systems but can also put customers who use your service at risk. Cyber attacks can lead not only to financial losses but also affect the public’s opinion of your company.

Most businesses focus on security to prevent traditional and known threats. They could lead to unauthorized access or data breaches by exploiting technical weaknesses within the app or the web.

Advanced application security solutions use AI to detect not only a wide range of hacking exploits but also often overlooked business logic attacks.

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