How Does AI-Powered Attack Surface Management Aid Security Analysts?

The task of Attack Surface Management is clear — discover, classify, and report.

This AI-powered tool evaluates any possible attack vectors (points that enable hackers to enter the system) and analyzes the findings to determine which high-risk weaknesses need patching up. It ends the cycle with the report.

What's more, it seeks and catalogs assets within and outside the attack surface that might be compromised.

The key component that enables it to evaluate vulnerable assets within the attack surface is artificial intelligence.

The relevance of the software has increased in light of the high number of security professionals that have been leaving the field.

Why are cybersecurity workers quitting, and how can AI-powered Attack Surface Management aid security analysts that are still at the forefront of defenses for many businesses?

Let's find out.

The Great Resignation in Cybersecurity

According to recent research, it has been uncovered that 45% of professionals considered quitting and 46% know at least one colleague that left the position due to a high level of stress in 2022.

Why are businesses having a hard time retaining talent in their cyber divisions?

Heavy workloads and poor work-life balance combined with pressures and responsibility to secure the key assets of a business have urged almost half of the cybersecurity professionals to quit their jobs.

According to Statista, the top five main culprits for the great resignation in the field are:

• Better job offers from other companies
• Lack of job incentives for employees
• Limited opportunities for promotions and development
• Sky high-stress levels
• Insufficient support from management

The increased number of resignations means that current teams are understaffed and overwhelmed more than before.

Over the last couple of years, the number of cyberattacks has increased, leaving the teams with perpetual ransomware threats and zero-day exploits.

Because of that, many companies expect higher availability from the members of their security teams.

So, how does AI-based management help?

Reduction in Number of Alerts

IT teams can get over 1,000 alerts per day. Not all of them are important, but the rest require attention because they indicate incidents such as successful ransomware, DDoS, or data breaches.

Overwhelmed with the continuous alerts (a lot of them coming from multiple dashboards), members of security teams are likely to discard them as false positives. That can result in major slip-ups that put companies at risk.

Attack Surface Management separates high-risk from low-risk issues. That feature aids analysts in prioritizing tasks by highlighting the assets that are most likely to endanger the infrastructure.

Risk-focused reports are automatically updated on the dashboard, letting the teams know where the vulnerabilities are at all times. That enables them to make quick conclusions on the severity of the exposed assets and find out where they are within the system.

Automated reports indicate:

• How many assets have been uncovered
• Which assets does the company have (classified by types)
• What is the overall score of the security (how strong is it)
• What type of assets have been compromised

Simplifying the Management of Complex Systems

An increased number of attacks has marked the last couple of years in cybersecurity. Another significant issue has also been that businesses have been targeted with new types of threats.

The high number of assets on both internal and external surfaces had made it difficult to keep control over the ever-growing surface.

How can you reduce the attack surface of a company?

Introducing more tools to the security infrastructure that has already increased in complexity seems counterintuitive. However, Attack Surface Management has been designed to simplify and facilitate the work of professionals.

Instead of having to deduce which alerts matter when they're coming from multiple controls, the latest version of the tool provides an overview of the entire attack surface at a glance.

Also, it doesn't evaluate and classify the assets within the organization's infrastructure.  It includes External Security Posture Management which uncovers internet-facing threats such as leaked employee credentials that could lead to unauthorized access.

Automation facilitated the management as well.

Repetitive tasks have been allocated to AI, leaving security professionals to dedicate more time towards work that seeks more human brain power — such as advanced threats.

Timely Discovery of Issues

Considering that artificial intelligence enables the tool to continually run in the background, testing any attack vectors and analyzing its findings, it can alert the teams early.

ASM tests the surface with tools such as automated red teaming that targets the possible assets as an attacker would.

Having the data available on the spot can speed up the time it takes teams to realize that they have to react but also aid them in mitigating the threat with the right protocols, tools, and techniques.

In case of a successful cyberattack, IT teams are left with even more work on their plates. Security has to be strengthened, the threat mitigated or isolated, and more.

An attack that is discovered late can set businesses back millions. According to Statista, the average cost of data breaches (that are often the result of successful hacking) in 2022 has been $4.34 million by far.

For businesses that are understaffed in the security department or lack sufficient defenses, this means it's questionable whether they will continue operating following the major breach.

Final Word

This useful tool won't solve the issues that have been leading to an increased number of cybersecurity professionals handing in their resignations.

However, it does aid experts still working in the industry to combat alert fatigue and replace numerous dashboards that demand their attention.

Also, it automates manual tasks such as testing analysis and creating a patching schedule based on the findings. With repetitive tasks outsourced, they have more resources to allocate toward complex and pressing issues.

Artificial intelligence also speeds up reaction (mitigation and remediation) time.

The data that is generated following the testing and analysis process aids teams in thinking quickly on their feet and dealing with the issue at hand.