Using AI for App Security: A Comprehensive Guide

Apps have become integral to businesses’ digital offerings, enabling customers to interact with them. While this is great for the company and the customers, it is another new gateway for cybercriminals to enter and access the business data.

This is why businesses are implementing better app security and exploring how AI can help them reduce coding errors, which might impact the overall safety of the company and the customer data.

The Need for AI in App?Security

As discussed above, app security is becoming increasingly crucial for companies. Even with great security professionals with a lot of experience in the market who can help detect and manage any security lapses in your business tech gateways, significant security breaches are still occurring over the internet.

The big challenge is that cybercriminals are using automated tools to figure out ways to break through security loopholes, and businesses are still very much dependent on humans to identify, assess and counter these hacks.

There is a limitation to what humans can do when the counterparty is relentless and works round the clock using automated tools. That’s why it’s essential to adopt AI for security to ensure that any threats, irrespective of the time, are immediately identified and responded to automatically.

Using AI, businesses can ensure that they are continuously staying ahead of the evolving threats that are changing daily. AI-based security systems can be set up constantly, always learning about all the new threats. Then, they can provide better security to the business and the app users.

How Can AI Tech Solve The Above Challenges?

As discussed above, you can see that AI has some advantages over human security professionals regarding app security. This is especially true when it comes to threat detection and response times. However, businesses should not consider this an alternative to hiring security professionals. AI is a tool that aims to assist the human team member to do more in their existing jobs.

Any regular security professional today has so much on their brain around threat detection and mitigation that it might hamper their ability to think about creative solutions to existing threats peacefully. When paired with AI, these individuals are more likely to quickly identify the threat and find a solution to the challenge.

These AI-based systems can be set up to check for new threats in real time and hence can identify any potential threat first and flag it up for professionals to look at it.

Some threats are less complicated to prevent, and these kinds of incidents can be identified and responded to directly by the AI using a set of predefined actions once it has been trained efficiently. This removes the stress of dealing with small threats for security professionals.

The AI system will use machine learning to learn to identify and respond to new threats continuously. Most loopholes in a system require a particular pattern or approach that needs to be executed for the cybercriminal to get past your app security; using machine learning AI tools can not just identify this pattern but also predict the potential routes or patterns that future cyber attackers might take to get past the existing security systems.

As part of their work, these AI tools can run predictive analysis to prioritize and understand the impact that each potential threat might have on your app. This will help both the cyber security personnel and the AI-based security tool to prioritize and proactively take steps to counter these potential threats even before they become a reality.

AI-Powered Security vs Traditional Security

We have already discussed how AI-based security systems can help out apps with better security. Let’s explore the differences between these AI-powered security systems and the traditional security systems currently used.

Adaptivity to?Threats

AI-powered security relies on continuous learning to understand the threats the system has previously faced, enabling AI-based systems to know how to adapt better to future threats. Adapting to new threats in traditional security systems is challenging, as it relies heavily on human input. Humans could easily understand and replicate solutions to threats they have

already studied, but adapting to newer threats or environments takes up a little more time when compared to an AI-powered security system.

Real-time Threat Detection

AI-powered security in apps can always be active to identify potential threats that might impact the app’s security and immediately trigger a warning or take appropriate responses to mitigate that threat.

Traditional security sometimes only triggers a warning if there is a threat, or sometimes, it does not trigger a warning at all. Cybersecurity professionals sometimes need to dig into the app to identify a threat.

Advanced Analytics

AI-powered security tools can analyze the previous threats to provide business owners and cybersecurity professionals with advanced information relating to the threats. This data can help train the AI and give cybersecurity professionals more details to prepare them for future attacks.

Traditional security models provide companies and cybersecurity professionals with more than the basic analytics, showcasing over-the-top data that might miss some of the advanced analytical data that AI-powered security tools can give the companies.

Scalability

AI-powered security tools can easily be scaled to whatever level businesses require. These tools can identify and respond to 100 and 10000 threats with just a few minor changes to the tool.

Traditional security models rely on human capabilities; hence, if there is a need to scale the operational capacity, the business needs to recruit new experts to work on cybersecurity,

which becomes quite a process, especially considering the time taken for the recruitment process.

Reducing False Positives

Sometimes, security tools trigger potential threats, which are false positive threats. In scenarios like this, an AI-based security system can quickly analyze the systems to check where the potential threat is. Once it does run that check, it can close the threat as a false positive.

When a false positive is triggered by a security tool while using a traditional security system, a cybersecurity professional will spend a few hours trying to understand the source of this threat. The time taken for the cybersecurity professionals to isolate the potential threat and then check if the threat is positive or a false positive is considerably more when compared to an AI-powered tool running this test.

Automation

Some large apps with considerable sensitive data need a more robust threat detection and mitigation process.

This is why there is a need to automate as much of the process as possible to ensure that these tests are run regularly at predefined intervals. This way, an AI-powered platform can run predefined tests to assess the threat levels for the apps and their data.

In a traditional setup, a cybersecurity professional will run a predefined set of tests using single or multiple tools, which can help them identify any potential threats. However, the possibility of automation in the traditional setup is low compared to AI-powered security systems.

Behavioral Analysis

AI-powered tools can not just analyze previous threats for data. Still, they can also identify various behavioral patterns which indicate the possibility of a threat to an app.

In the traditional set of security tools, the onus of identifying behavioral patterns is on the cybersecurity professionals, who sometimes might not have the keen eye to identify unique patterns in the behaviors of hackers in the past. Using traditional security tools, cybersecurity professionals may be unable to locate a specific behavior that could lead to a threat in the future.

Predictive Security

An AI-powered security tool can predict and analyze potential threats and, hence, can easily create solutions for predictive security measures, which ensure that the app is always secure and is not impacted by cyber-attacks.

In traditional security systems, it would have been a little challenging to create predictive security solutions as all the data would have been available based on previous threat information (which has already been responded to and closed) and also, a lot of bias can impact the decision making of individuals in charge of creating these predictive security models.

Case Studies on AI-Powered App?Security

Implementing AI-powered app security systems has helped many businesses improve the efficiency and safety of applications and company and user data protection.

Companies like MediaMarktSaturn Retail Group have used AI-powered security tools to reduce the cost of security expenditure. This is possible because they move from manual threat detection and response to automated threat detection and response, which uses AI to speed up the process.

Another example is a company named zPower, which used an AI tool to identify malicious software on an employee’s computer and alerted the cybersecurity team before it increased to a severe threat.

AI-based tools also help in isolating and responding to fraud. PetSmart has used AI-based tools to identify fraudulent transactions and save lots of money for the business.

Wrapping up AI-Powered App?Security

All the above case studies showcase that AI-powered security tools can speed up the detection and response process for businesses, which keeps the company and its users safe and saves a lot of money for the business.