How AI and Digital Process Automation Can Make Your Business More Secure

Cybersecurity attacks are on the rise. In fact, 2016 was a record year for security breaches. According to the Identity Theft Resource Center, U.S. companies and government agencies experienced about 1,093 data breaches last year (up by 40% from the previous year).

The sheer number of breaches makes it increasingly difficult for security teams to respond effectively using traditional tools. The speed at which vulnerabilities are targeted also reaffirms that it’s just not humanly possible (anymore) to keep up with cybersecurity threats.

This is what makes Artificial Intelligence (AI) and Machine Learning (ML) critical to keep businesses secure. Furthermore, low-code digital process automation tools can also ensure that security is maintained while improving productivity.

AI enabled self-learning and automation capabilities can also help increase the effectiveness of your defenses and reduce cost. This is because this technology will be constantly working to identify patterns and anomalies associated with malicious content to enhance security across endpoints, users, networks, IoT, and the cloud.

Let’s dive in deeper to ascertain how AI and digital process automation effectively responds to cybersecurity threats.

AI improves identification of threats

In the past, security teams just had to focus on network and endpoint protection. In the digital age, the attack surface that has to be monitored and protected has expanded significantly and will continue to grow rapidly.

Today, enterprises have to deal with a variety of applications, cloud services, mobile devices, and “smart things.” This makes maintaining security an uphill battle.

Essentially, this adds to the existing problem of trying to effectively protect and manage volume, velocity, and the complexity of data that is continuously generated by multiple IT and security tools within the business.

The data generated from multiple sources must be analyzed, normalized, and remediations efforts have to be prioritized. However, the more tools you add to achieve this, the harder it will be to manage.

Traditionally, companies employed legions of security professionals to comb through an enormous amount of data to find latent threats. But this approach was expensive and took months, so while they combed through the data, hackers were able to keep exploiting vulnerabilities and extracted data.

AI is a highly-effective response to this as it enables businesses to effectively break down existing silos. By automating traditional security operations, enterprises have been able to effectively supplement scarce cybersecurity talent.

In this scenario, you can say that human-interactive ML engines are able to achieve the following:

  • Aggregation of data across different data types
  • Map assessment data to compliance requirements
  • Normalize the information to rule out false-positives, duplicates, and enrich data attributes

Digital process automation ensures that security is maintained in real-time

As businesses focus on rapidly delivering enhanced user experiences to customers and employees alike, security can sometimes take a backseat. But this approach can leave the entire business vulnerable to security breaches.

One way to tackle this problem is to take advantage of a low-coding approach to application development. When you utilize low-code development platforms like Pulpstream for digital process automation, you can focus on building apps without worrying about security.

As you drag and drop functions to build your apps rapidly (to respond to business problems), you can develop your digital business solutions knowing that human security experts together with AI technology are working on keeping your company secure, every step of the way.

Risk assessment in real-time

Risk assessment can be executed in real time once internal security intelligence is contextualized with external threat data. This can come in the form of past exploits, threat actors, malware, and reputation intelligence.

Once these findings are correlated with business critical processes to determine gaps in security, you can take necessary steps to remedy the situation in real-time. Not knowing where your vulnerabilities are, makes remediation efforts almost impossible.

So this makes it more important to have advanced algorithms play a big role in driving the appropriate response to individual risks.

Remediation

When it comes to cybersecurity remediation, it’s important to use a risk-based cybersecurity concept as a blueprint. This will enable the implementation of automated processes for proactive security incident notification and intervention.

Once predefined rules and thresholds have been established, businesses can respond with remediation action in a timely manner. AI in this context helps to significantly reduce the time-to-remediations.

However, this doesn’t mean that human security professionals won’t have a role to play in the future. This is because AI will be the first line defense conducting an assessment of security data.

This, in turn, will enable security analysts to focus on advanced security threats than wasting time performing tactical data crunching.

In the months and years to come, you can expect to see AI and digital process automation play a critical role in keeping businesses secure. As more users, mobile devices, and IoT gadgets get on the network, it’s now more important than ever to get all the help available to maintain enhanced security.